Providing Context in Prompts

Introduction

A prompt tells an AI model what task it should perform. However, the task instruction alone may not provide enough information for the model to produce an accurate, relevant, and usable response.

Context gives the model the background information required to understand the task correctly.

Consider the following prompt:

Write a project status report.

The model does not know:

• Which project the report is about
• Who will read the report
• What work has been completed
• Which problems currently exist
• What reporting period should be covered
• Whether the report should be technical or business-focused
• What format should be used

A context-rich version provides this missing information:

You are preparing a weekly project status report for senior management.
The project is an e-commerce platform migration from a monolithic application to microservices.
The development team completed the product catalogue service and payment service integration this week.
The order service is delayed because of database performance issues.
The report should focus on progress, risks, business impact, and next-week priorities.
Write the report in a concise professional format with clear headings.

The second prompt gives the model enough information to understand the project, audience, current situation, expected emphasis, and required output.

Providing context does not mean adding large amounts of information. Effective context includes only the details that influence the response.

Chapter Overview

This chapter explains how to provide useful context in prompts. It covers:

• The meaning of prompt context
• The relationship between context and response quality
• Different categories of context
• Relevant and irrelevant contextual information
• Correct placement of context inside a prompt
• Problems caused by excessive or missing context
• Methods for prioritising contextual information
• A checklist for evaluating context quality

Learning Objectives

After completing this chapter, you should be able to:

• Define prompt context clearly
• Identify which information an AI model needs before completing a task
• Distinguish between background, business, user, technical, situational, and domain context
• Remove irrelevant information from prompts
• Place contextual information in a logical position
• Prevent context overload
• Detect problems caused by missing context
• Prioritise important information
• Evaluate context using a practical quality checklist
• Create context-rich prompts for business, technical, educational, and content-generation tasks

Prerequisites

Before studying this chapter, you should understand:

• What a prompt is
• The difference between instructions and input data
• Basic prompt structure
• Output-format requirements
• Constraints and rules
• Target audience
• Role or persona prompting

Key Terminology

What Is Prompt Context?

Prompt context is the background information that helps an AI model understand:

• What the task is about
• Why the task is being performed
• Who the response is intended for
• What circumstances affect the task
• Which facts should guide the response
• Which assumptions should or should not be made
• What level of knowledge the response should reflect

Context provides meaning around an instruction.

An instruction tells the model what to do:

Explain dependency injection.

Context tells the model how the explanation should be understood:

The reader is a beginner Java developer who understands classes and interfaces but has not worked with Spring Framework.

Combined prompt:

The reader is a beginner Java developer who understands classes and interfaces but has not worked with Spring Framework.
Explain dependency injection using a simple Java example.
Avoid advanced Spring configuration.
Include the problem it solves, how it works, and one real-world analogy.

The context changes the depth, terminology, examples, and structure of the answer.

Context Is Not the Same as Instruction

Instruction:

Create a marketing plan.

Context:

The business is a newly launched online Java interview preparation platform targeting software developers in India.

The instruction defines the action.

The context defines the environment in which that action must be performed.

Context Is Not the Same as Input Data

Input data is the specific content being processed:

Customer feedback:
The application is useful, but the checkout process is slow and confusing.

Context explains how the data should be interpreted:

This feedback was collected from first-time mobile users during a usability test.

The model may produce different conclusions when it knows that the feedback came from first-time mobile users instead of experienced desktop users.

Basic Context Formula

A useful prompt context often answers five questions:

1. What is happening?
2. Why is the task required?
3. Who is involved?
4. What facts affect the result?
5. What assumptions or limitations apply?

Why Context Improves Responses

AI models generate responses by interpreting patterns in the prompt and predicting appropriate output. When the prompt contains insufficient information, the model must rely on general assumptions.

These assumptions may not match the user's actual requirements.

Context improves responses in several ways.

1. Context Reduces Ambiguity

Ambiguous prompt:

Write an introduction about Java.

Possible interpretations include:

• Introduction for complete beginners
• Introduction for experienced Python developers
• Introduction for an interview tutorial
• Introduction for a university assignment
• Introduction for a product brochure
• Introduction for technical documentation

Context-rich prompt:

Write an introductory section about Java for beginner programming students.
The students have no previous coding experience.
Explain what Java is, where it is used, and why it remains important.
Avoid advanced JVM terminology.

The context reduces the number of possible interpretations.

2. Context Improves Relevance

Without context, the model may include technically correct but practically irrelevant information.

For example:

Recommend a database for an application.

The correct recommendation depends on:

• Type of application
• Data structure
• Query patterns
• Expected traffic
• Consistency requirements
• Development skills
• Infrastructure limitations
• Budget

Context-rich version:

We are building a multi-tenant SaaS billing application.
The system will store customers, subscriptions, invoices, and payments.
Transactions require strong consistency.
The development team uses Java and Spring Boot.
The application is expected to support approximately 100,000 active customers.
Recommend a primary database and explain the decision.

The response can now focus on suitable database characteristics instead of providing a generic list.

3. Context Controls Technical Depth

The same subject can be explained at different levels.

Beginner context:

Explain REST APIs to a student who understands basic HTTP but has never developed a web service.

Advanced context:

Explain REST API versioning strategies to a senior backend engineer working with distributed Spring Boot services.

The first response should explain fundamentals.

The second response should discuss trade-offs such as:

• URI versioning
• Header versioning
• Backward compatibility
• Deprecation
• Consumer migration
• API gateway policies

4. Context Produces Better Examples

Models select examples based on the information available in the prompt.

Generic instruction:

Explain the Factory pattern.

Context-rich instruction:

Explain the Factory design pattern to a Java developer building a payment system.
Use CreditCardPayment, UpiPayment, and NetBankingPayment as implementation examples.

The model can generate examples directly related to the user's application.

5. Context Aligns the Response with the Audience

The same report should not be written identically for:

• Developers
• Customers
• Senior management
• Investors
• Support teams
• Government regulators

Audience context affects:

• Vocabulary
• Level of detail
• Focus
• Tone
• Explanation style
• Use of technical terms
• Recommended actions

6. Context Reduces Unwanted Assumptions

When information is missing, the model may infer:

• A target audience
• A business objective
• A technology stack
• A geographic market
• A time period
• A desired output length
• A preferred tone

Good context replaces uncertain assumptions with explicit information.

7. Context Improves Consistency

When a prompt contains stable background information, the model is more likely to maintain consistent:

• Terminology
• Business rules
• Character behaviour
• Technical architecture
• Formatting conventions
• Decision criteria

This is especially important in multi-step workflows and multi-turn conversations.

Background Context

Background context describes the broader history, environment, or reason behind the task.

It answers questions such as:

• What led to the current task?
• What has already happened?
• What is the larger project?
• Why is the request being made?
• Which previous decisions affect the response?

Example Without Background Context

Suggest improvements for this website.

This prompt does not explain the purpose of the website or the existing problem.

Example With Background Context

We operate a free programming education website containing Java, Python, and prompt engineering tutorials.
Most pages include detailed articles and interactive interview-preparation tools.
Google AdSense previously rejected the website because of insufficient value.
Suggest improvements that can increase content usefulness, user engagement, and perceived originality.

The background helps the model focus on improvements related to:

• Content depth
• Interactive value
• User experience
• Originality
• Site structure
• Advertising compliance

When Background Context Is Useful

Background context is useful when:

• The task is part of a larger project
• Previous events affect the decision
• Earlier attempts failed
• A problem has developed over time
• The model must understand the reason behind the request
• Existing decisions must be preserved
• The response should continue from previous work

Background Context Template

The current task is part of [project or initiative].
The project was created to [purpose].
So far, [completed work or previous events].
The current problem is [problem].
The requested output will be used for [intended use].

Example

The current task is part of a Java interview preparation platform.
The platform already includes MCQ tests, output-based questions, and project interview questions.
Users can practise questions, review wrong answers, and track progress.
The next feature should improve revision without requiring account registration.
Suggest a suitable feature and explain its workflow.

Business Context

Business context explains the commercial or organisational environment related to the task.

It may include:

• Business model
• Product or service
• Target customers
• Revenue source
• Competitive position
• Organisational goals
• Budget limitations
• Operational constraints
• Success metrics
• Market location
• Brand positioning

Why Business Context Matters

A technically valid recommendation may still be unsuitable for a business.

For example, a solution may be:

• Too expensive
• Too complex for the current team
• Difficult to maintain
• Unsuitable for the target customer
• Misaligned with the revenue model
• Slow to launch
• Incompatible with legal requirements

Business context allows the model to evaluate practical suitability.

Generic Prompt

Suggest features for an education website.

Business Context Prompt

We operate a free programming education website for students and software developers in India.
The primary revenue source is display advertising.
The website does not currently require user registration.
The development team consists of one developer using PHP, JavaScript, HTML, and CSS.
Suggest features that increase repeat visits and page engagement without requiring paid APIs or a complex backend.

The model should prioritise features such as:

• Revision tools
• Saved progress using local storage
• Topic quizzes
• Daily challenges
• Interactive flashcards
• Learning checklists
• Search and filtering
• Difficulty-based practice

It should avoid recommending expensive or operationally heavy systems unless justified.

Important Business Context Elements

Business objective

Increase returning visitors and average session duration.

Target customer

Beginner and intermediate software developers preparing for interviews.

Revenue model

The platform earns primarily through display advertisements.

Resource limitation

The application is maintained by one developer.

Budget

New features should avoid paid APIs.

Success criteria

The feature should increase practice sessions per user.

Business Context Template

Business type: [type of organisation]
Product or service: [offering]
Target customer: [customer group]
Primary objective: [business goal]
Revenue model: [how the business earns]
Available resources: [team, technology, budget]
Important constraints: [limitations]
Success metric: [how success will be measured]

User Context

User context describes the person or group that will use the response, product, explanation, or recommendation.

It may include:

• Knowledge level
• Experience
• Role
• Goals
• Preferences
• Language
• Accessibility needs
• Geographic location
• Device type
• Time limitations
• Existing skills
• Common problems

Why User Context Matters

A response is valuable only when the intended user can understand and apply it.

For example, instructions written for a senior DevOps engineer may be unusable for a beginner student.

Example Without User Context

Explain Docker.

Example With User Context

Explain Docker to a Java developer who has built Spring Boot applications but has never used containers.
Focus on images, containers, Dockerfiles, ports, and application deployment.
Avoid Kubernetes concepts.

This context defines:

• Existing knowledge
• Missing knowledge
• Relevant concepts
• Unnecessary concepts
• Appropriate terminology

Types of User Context

Experience level

The user is a beginner with no programming experience.

Professional role

The user is a Java backend developer.

Goal

The user is preparing for technical interviews.

Time limitation

The user has seven days to prepare.

Preferred learning style

The user learns better through short explanations and practical examples.

Language requirement

Use simple English and explain technical terms when first introduced.

Device context

The user accesses the website mainly from a mobile phone.

User Context Template

The intended user is [role or profile].
The user currently understands [existing knowledge].
The user does not yet understand [knowledge gap].
The user wants to achieve [goal].
The user prefers [format, language, or learning style].
The user is limited by [time, device, budget, or accessibility constraint].

Technical Context

Technical context describes the technologies, architecture, environment, data, standards, and system limitations relevant to the task.

It may include:

• Programming language
• Framework
• Runtime version
• Database
• Operating system
• Deployment environment
• Architecture
• Existing code structure
• API contracts
• Performance requirements
• Security requirements
• Compatibility requirements
• Dependency restrictions

Why Technical Context Matters

Technical recommendations depend heavily on the existing environment.

For example, asking:

Create authentication for my application.

does not identify:

• Application type
• Backend technology
• Frontend technology
• Existing authentication method
• Database
• Session strategy
• Security requirements
• Deployment environment

Technical Context Prompt

The application uses Java 21, Spring Boot 3, Spring Security 6, PostgreSQL, and a React frontend.
The backend exposes REST APIs.
Authentication should use short-lived access tokens and refresh tokens.
Users can log in using email and password.
Create a high-level authentication design covering login, token generation, token validation, refresh, logout, and token revocation.

The model can now produce a design compatible with the specified stack.

Important Technical Context Categories

Technology stack

Java 21, Spring Boot 3, PostgreSQL, and Redis.

Architecture

The system uses independently deployable microservices.

Deployment environment

Services are deployed as Docker containers on AWS ECS.

Current problem

Database response time increases during peak traffic.

Operational requirement

The solution must work without application downtime.

Security requirement

Sensitive data must be encrypted in transit and at rest.

Compatibility requirement

The solution must support Java 17 and later.

Prohibited dependency

Do not use third-party paid APIs.

Technical Context Template

Application type: [application category]
Technology stack: [languages, frameworks, databases]
Architecture: [monolith, microservices, serverless, event-driven]
Deployment environment: [local, cloud, container, shared hosting]
Existing implementation: [current design]
Current problem: [technical issue]
Required compatibility: [versions or standards]
Constraints: [performance, security, cost, dependency rules]

Situational Context

Situational context explains the immediate circumstances surrounding a task.

It focuses on what is happening now.

Examples include:

• A production outage
• An upcoming interview
• A customer complaint
• A delayed project
• A failed deployment
• A sudden traffic increase
• A security incident
• A deadline
• A negotiation
• A change in business policy

Example Without Situational Context

Write a customer response.

Example With Situational Context

A customer reported that payment was deducted twice during checkout.
The support team confirmed that one transaction was successful and the second transaction is pending reversal by the bank.
The reversal usually takes five to seven business days.
Write a professional customer response that acknowledges the issue, explains the status clearly, and avoids blaming the bank.

The situation determines the content and tone of the response.

Situational Context Characteristics

Situational context is usually:

• Time-sensitive
• Event-specific
• Temporary
• Directly connected to an immediate decision
• Important for tone and urgency

Situational Context Template

Current situation: [what is happening]
Triggering event: [what caused it]
People affected: [stakeholders]
Immediate impact: [effect]
Required action: [what must happen]
Deadline or urgency: [time requirement]
Sensitive points: [what should be handled carefully]

Domain Context

Domain context provides specialised information related to a particular field, profession, industry, or knowledge area.

Examples of domains include:

• Healthcare
• Banking
• Insurance
• Education
• Software engineering
• Cybersecurity
• Legal services
• E-commerce
• Manufacturing
• Human resources
• Digital marketing

Why Domain Context Matters

Terms may have different meanings in different domains.

For example, the word "claim" may refer to:

• An insurance claim
• A legal claim
• A statement requiring evidence
• A token claim in authentication
• A warranty claim

The model needs domain context to interpret terminology correctly.

Example Without Domain Context

Explain claim validation.

Insurance Domain Context

In the context of health insurance processing, explain claim validation.
Cover policy status, patient eligibility, provider verification, procedure codes, duplicate claims, and coverage limits.

Software Security Domain Context

In the context of JWT-based authentication, explain claim validation.
Cover issuer, audience, expiration, subject, signature, and custom permission claims.

The phrase is the same, but the required explanation is completely different.

Domain Context May Include

• Domain terminology
• Industry processes
• Regulations
• Standard documents
• Common roles
• Business rules
• Risk factors
• Compliance requirements
• Accepted practices
• Domain-specific data formats

Domain Context Template

Domain: [industry or subject]
Relevant process: [business or technical process]
Important terminology: [terms and meanings]
Applicable rules: [standards, policies, regulations]
Common stakeholders: [roles]
Domain-specific risks: [risks]
Expected professional practice: [accepted approach]

Relevant vs Irrelevant Context

Not every available detail should be included in a prompt.

Relevant context influences the model's interpretation, decision, structure, language, or output.

Irrelevant context does not materially affect the requested result.

Relevant Context

For a database recommendation, relevant context includes:

• Data type
• Query patterns
• Transaction requirements
• Scalability requirements
• Team skills
• Budget
• Availability needs
• Existing infrastructure

Irrelevant Context

For the same task, irrelevant context may include:

• The colour of the application logo
• The founder's favourite programming language
• The office seating arrangement
• An unrelated marketing campaign
• Historical details with no effect on the database decision

Relevance Test

Before adding a detail, ask:

1. Will this information change the response?
2. Does it help the model choose between possible answers?
3. Does it remove a likely misunderstanding?
4. Does it affect tone, depth, format, or recommendation?
5. Is it required to satisfy a constraint?
6. Does it explain an important term?
7. Does it prevent an incorrect assumption?

When the answer to all questions is no, the information is probably unnecessary.

Example of Irrelevant Context

Our company was founded in 2018.
Our office walls are blue.
We recently changed our logo.
The development team prefers coffee.
We use Java and Spring Boot.
Our API response time has increased from 300 milliseconds to 2 seconds.
The problem occurs mainly when the order table contains more than 10 million records.
Suggest a database performance investigation plan.

Only the technical details affect the requested investigation.

Improved Version

We use Java, Spring Boot, JPA, and PostgreSQL.
API response time has increased from approximately 300 milliseconds to 2 seconds.
The issue occurs mainly for queries involving the order table, which contains more than 10 million records.
Suggest a step-by-step database performance investigation plan.

Context Placement

Context should be placed where the model can clearly associate it with the task.

A common and effective prompt order is:

1. Role
2. Background or context
3. Task instruction
4. Input data
5. Constraints
6. Output format
7. Completion criteria

Recommended Structure

Role:
You are a senior Java performance engineer.
Context:
A Spring Boot application has slow order-search APIs.
PostgreSQL contains more than 10 million order records.
The issue appears during peak traffic.
Task:
Create a performance investigation plan.
Requirements:
Cover application metrics, SQL logging, execution plans, indexes, connection pooling, caching, and load testing.
Output format:
Present the plan as numbered diagnostic steps.
Completion condition:
Each step must include its purpose, evidence to collect, and possible corrective action.

Place Context Before the Task When It Defines Interpretation

The audience consists of non-technical business managers.
Explain why the software release was delayed.

The model knows how to frame the explanation before processing the task.

Place Input-Specific Context Near the Input

Review the following customer feedback.
The feedback was collected only from mobile users who abandoned checkout.
Customer feedback:
[feedback data]

The collection condition should appear immediately before the data because it affects interpretation.

Place Global Context Near the Beginning

Global context applies to the entire response.

Examples:

• Audience
• Business objective
• Domain
• Technology stack
• Overall project background

Place Local Context Near the Relevant Instruction

Local context applies only to one section or subtask.

Example:

For the security section, assume that the application stores payment information.
For the performance section, assume peak traffic of 5,000 requests per minute.

Avoid Scattering Related Context

Poor structure:

Write an API design.
Use Java.
The system handles payments.
Add endpoint examples.
We use Spring Boot.
Security is important.
The frontend is React.
Use JSON.
Include validation.

Improved structure:

Technical context:
The system is a payment application built with Java, Spring Boot, and a React frontend.
The backend communicates with the frontend using JSON REST APIs.
Task:
Design APIs for creating, retrieving, and cancelling payments.
Requirements:
Include endpoint paths, HTTP methods, request bodies, response bodies, validation rules, error responses, and security considerations.

Context Overload

Context overload occurs when a prompt contains more information than the task requires.

More context does not always produce a better answer.

Excessive context can:

• Hide the primary objective
• Introduce conflicting facts
• Increase processing difficulty
• Cause the model to focus on minor details
• Reduce output consistency
• Consume the available context window
• Increase token usage and cost
• Make the prompt difficult to maintain
• Cause important instructions to be overlooked

Example of Context Overload

Our company started in 2017.
The founder first created a desktop application.
The company changed offices in 2019.
We used PHP in the first version.
One developer later learned Java.
The original logo was green.
We now have twelve employees.
The marketing team uses social media.
Our customers are mainly from India.
Some employees work remotely.
We use Spring Boot 3 and PostgreSQL.
The order API becomes slow after traffic exceeds 2,000 requests per minute.
CPU usage remains below 50 percent, but database connections reach the maximum pool size.
Suggest the most likely cause and an investigation plan.

Most of the historical information is irrelevant.

Improved Version

Our order service uses Spring Boot 3, HikariCP, and PostgreSQL.
The order API becomes slow when traffic exceeds 2,000 requests per minute.
Application CPU usage remains below 50 percent.
Database connections reach the configured maximum pool size during the slowdown.
Identify the most likely causes and provide a prioritised investigation plan.

Signs of Context Overload

A prompt may contain excessive context when:

• The instruction is difficult to locate
• Several details do not affect the task
• The same fact appears repeatedly
• Multiple unrelated objectives are mixed together
• Historical information is longer than the actual task
• Important constraints are buried inside paragraphs
• The model produces inconsistent or unfocused output
• The prompt requires repeated rereading to understand

How to Reduce Context Overload

1. Write the primary objective first.
2. List only facts that affect the output.
3. Remove repeated information.
4. Separate global and task-specific context.
5. Summarise long documents before using them as context.
6. Use headings or labels.
7. Prioritise critical constraints.
8. Move reference data into a separate section.
9. Remove examples that introduce unrelated patterns.
10. Split complex workflows into multiple prompts when necessary.

Missing Context Problems

Missing context forces the model to guess.

The response may still sound confident and well-written even when it is based on incorrect assumptions.

Common Missing Context Problems

1. Wrong Audience Level

Prompt:

Explain multithreading.

The response may be too advanced for a beginner or too basic for an experienced developer.

2. Wrong Business Objective

Prompt:

Improve this landing page.

The model does not know whether the objective is:

• More sales
• More registrations
• More advertisement views
• Better SEO
• Better trust
• More course enrolments

3. Incorrect Technical Assumptions

Prompt:

Fix this authentication issue.

The model does not know:

• Authentication mechanism
• Framework
• Error message
• Current code
• Expected behaviour
• Security requirements

4. Inappropriate Tone

Prompt:

Write a response to the customer.

The response could be too casual, defensive, technical, or impersonal.

5. Unusable Recommendation

Prompt:

Recommend a deployment platform.

The recommendation may ignore:

• Budget
• Region
• traffic
• team experience
• compliance requirements
• existing hosting environment

6. Incorrect Scope

Prompt:

Create a tutorial on Spring Boot.

The model does not know whether the tutorial should cover:

• One page
• A complete course
• Interview preparation
• REST APIs
• Microservices
• Beginners
• Experienced Java developers

Questions That Reveal Missing Context

Before submitting a prompt, check whether the model knows:

• The intended audience
• The purpose of the output
• The current situation
• The relevant background
• The technology or domain
• The expected scope
• The important limitations
• The required format
• The definition of success
• The source of input data

Example Improvement

Unclear prompt:

Review my code.

Context-rich prompt:

Review the following Java 21 Spring Boot service method.
The method processes payment callbacks from an external provider.
Duplicate callbacks may be received for the same transaction.
The method must remain idempotent and thread-safe.
Review it for concurrency issues, transaction handling, exception handling, security, and database consistency.
Do not rewrite the entire class.
Return findings in order of severity.

Context Prioritisation

Context prioritisation means identifying which information is most important and arranging it so the model gives it appropriate attention.

Not all context has equal importance.

Priority Levels

Critical Context

Critical context directly determines whether the response is correct.

Examples:

• The task objective
• Mandatory business rules
• Security requirements
• Legal restrictions
• Target audience
• Required technology
• Prohibited actions
• Essential input facts

Supporting Context

Supporting context improves quality but is not always required for basic correctness.

Examples:

• Previous attempts
• User preferences
• Style expectations
• Secondary performance goals
• Additional examples
• Organisational background

Optional Context

Optional context may be helpful but has little effect on the result.

Examples:

• Historical information unrelated to the decision
• Personal preferences that do not affect output
• General company description
• Non-essential statistics

Context Prioritisation Method

Step 1: Define the Primary Objective

Create a troubleshooting plan for a slow database query.

Step 2: Identify Facts Required for Correctness

Database: PostgreSQL
Application: Spring Boot
Data size: 10 million records
Problem query: Order search
Symptom: Response time increased from 300 milliseconds to 2 seconds

Step 3: Add Important Constraints

Production access is read-only.
The investigation must not cause downtime.

Step 4: Add Useful Supporting Information

The issue is more frequent during peak traffic.
Connection pool usage is high.

Step 5: Remove Non-Influential Details

Remove information about:

• Company history
• Logo design
• Unrelated modules
• Team social activities
• Previous technologies no longer in use

Priority-Based Prompt Structure

Primary objective:
Diagnose the likely causes of the slow order-search API.
Critical context:
The application uses Spring Boot 3, JPA, and PostgreSQL.
The order table contains approximately 10 million records.
Search response time increased from 300 milliseconds to 2 seconds.
Constraints:
Production access is read-only.
The investigation must not interrupt live traffic.
Supporting context:
The issue occurs mostly during peak hours.
The database connection pool approaches maximum usage.
Required output:
Provide a prioritised investigation plan with evidence to collect and possible fixes.

Use Explicit Priority Language

Useful phrases include:

Treat data security as the highest priority.
Prioritise solutions that require minimal infrastructure changes.
Focus primarily on database causes.
Consider application-level causes only after database checks.
The response must follow the compliance requirements even when they conflict with convenience.
Use the provided business rules as the authoritative source.
Ignore historical information that does not affect the current design.

Practical Context Framework

A reusable context framework can be built using the following components:

1. Situation

What is happening?

A Java application is experiencing slow API responses.

2. Background

What led to the current situation?

Response time increased after the order table grew beyond 10 million records.

3. Objective

What result is required?

Identify likely causes and create an investigation plan.

4. Audience

Who will use the result?

The plan will be used by Java developers and database administrators.

5. Domain

Which professional or technical field applies?

The task concerns backend performance engineering and PostgreSQL optimisation.

6. Constraints

What limitations must be respected?

Production access is read-only and downtime is not allowed.

7. Success Criteria

What makes the response complete?

The plan must include application metrics, SQL analysis, execution plans, index evaluation, connection pooling, and load testing.

Complete Framework Example

Situation:
A Spring Boot order-search API is responding slowly during peak traffic.
Background:
Response time increased after the PostgreSQL order table grew beyond 10 million records.
Objective:
Identify likely causes and create a structured investigation plan.
Audience:
The output will be used by Java developers and database administrators.
Technical context:
The application uses Java 21, Spring Boot 3, JPA, HikariCP, and PostgreSQL.
Constraints:
Production access is read-only.
The investigation must not interrupt live traffic.
Success criteria:
Cover application metrics, SQL logging, execution plans, indexes, connection pooling, caching, and load testing.
Output format:
Present the investigation as prioritised numbered steps.
Include the purpose, evidence, and possible corrective action for each step.

Step-by-Step Process for Adding Context

Step 1: Write the Task in One Sentence

Create a beginner-friendly explanation of Java exception handling.

Step 2: Identify the Audience

The reader understands variables, methods, and classes but has not used exceptions.

Step 3: Identify the Purpose

The explanation will be used in an online Java tutorial.

Step 4: Add Domain or Technical Information

Use Java 21 syntax and standard exception-handling practices.

Step 5: Add Scope

Cover checked exceptions, unchecked exceptions, try, catch, finally, throw, and throws.

Step 6: Add Constraints

Do not cover custom exception hierarchies or framework-level exception handling.

Step 7: Add Output Requirements

Include definitions, syntax, practical examples, common mistakes, and a comparison table.

Final Prompt

Create a beginner-friendly explanation of Java exception handling.
The reader understands variables, methods, and classes but has not used exceptions.
The content will be published in an online Java tutorial.
Use Java 21 syntax and standard exception-handling practices.
Cover checked exceptions, unchecked exceptions, try, catch, finally, throw, and throws.
Do not cover custom exception hierarchies or framework-level exception handling.
Include definitions, syntax, practical examples, common mistakes, and a comparison table.

Context Examples by Task Type

Content Writing Example

Weak prompt:

Write an article about prompt engineering.

Improved prompt:

Write a technical article about prompt engineering for software developers who are new to generative AI.
The article will be published as part of a free online tutorial.
Explain prompt structure, instructions, context, input data, constraints, output formats, and success criteria.
Use practical Java, SQL, and business examples.
Keep the language easy to understand without removing technical accuracy.

Software Development Example

Weak prompt:

Create a login API.

Improved prompt:

Create a login REST API for a Spring Boot 3 application using Java 21.
Users log in with email and password.
User records are stored in PostgreSQL.
Passwords are encoded with BCrypt.
Successful authentication should return a JWT access token and refresh token.
Use constructor injection.
Include controller, service, request DTO, response DTO, validation, and exception handling.
Do not include frontend code.
Each code comment must remain on a single line.

SQL Example

Weak prompt:

Optimise this query.

Improved prompt:

Analyse the following PostgreSQL query used by an order-search API.
The orders table contains approximately 10 million rows.
Users commonly filter by customer_id, status, and created_at.
Results are sorted by created_at in descending order and limited to 50 rows.
The current query takes approximately two seconds during peak traffic.
Explain possible bottlenecks, recommend indexes, and provide a revised query.
Do not assume that a new database can be introduced.

Business Analysis Example

Weak prompt:

Analyse this product.

Improved prompt:

Analyse a free online interview-preparation platform for Java developers in India.
The platform offers MCQs, output-based questions, flashcards, project interview questions, and managerial-round practice.
Revenue comes primarily from display advertising.
The product is maintained by one developer.
Evaluate user value, engagement potential, content differentiation, monetisation risks, and practical next steps.

Customer Support Example

Weak prompt:

Reply to this complaint.

Improved prompt:

A customer reports that a subscription payment was deducted but the premium account was not activated.
Payment records show a successful transaction, but the account update failed because of a temporary database error.
The technical team can activate the account manually within two hours.
Write a professional support response.
Acknowledge the inconvenience, confirm that payment is safe, explain the next action, and avoid exposing internal technical details.

Context Quality Checklist

Use the following checklist before submitting a context-rich prompt.

Relevance

• Does every contextual detail influence the response?
• Have unrelated historical details been removed?
• Is the context directly connected to the task?
• Are examples relevant to the requested domain?
• Does the prompt avoid unnecessary personal information?

Completeness

• Is the primary objective clear?
• Is the intended audience identified?
• Is the purpose of the response explained?
• Is the current situation described?
• Are important technical or business facts included?
• Are required constraints stated?
• Is the expected output format defined?
• Is the completion condition clear?

Accuracy

• Are all facts correct?
• Are technology versions accurate?
• Are business rules current?
• Are measurements and quantities reliable?
• Are assumptions clearly marked?
• Does the context avoid unsupported claims?

Clarity

• Is the context easy to scan?
• Are related facts grouped together?
• Are ambiguous terms defined?
• Are sentences direct and specific?
• Is the primary task easy to locate?
• Are instructions separated from reference information?

Priority

• Are critical facts placed before minor details?
• Are mandatory rules clearly identified?
• Are prohibited actions explicit?
• Are conflicting requirements resolved?
• Is the most important objective emphasised?
• Is the authoritative source identified when multiple references exist?

Scope

• Is the task boundary clear?
• Does the model know what to include?
• Does the model know what to exclude?
• Is the expected depth defined?
• Is the time period specified when relevant?
• Is the geographic or market scope defined when necessary?

Audience Alignment

• Is the user's knowledge level clear?
• Is the professional role identified?
• Is the preferred language or tone defined?
• Are accessibility or device limitations relevant?
• Is the expected terminology suitable for the audience?

Technical Quality

• Is the technology stack specified?
• Is the deployment environment described?
• Are compatibility requirements included?
• Are performance expectations measurable?
• Are security requirements explicit?
• Are dependency limitations mentioned?
• Is the existing implementation explained sufficiently?

Context Size

• Is the context shorter than necessary rather than longer?
• Has repeated information been removed?
• Can a long reference document be summarised?
• Should the task be split into smaller prompts?
• Are important instructions visible instead of buried?
• Is every included detail worth its token cost?

Context Quality Scoring Method

A prompt can be evaluated using a simple scoring system.

Maximum score: 20

Interpretation:

• 17-20: Strong context
• 13-16: Usable but can be improved
• 9-12: Important context is missing
• 0-8: High risk of generic or incorrect output

Common Mistakes When Providing Context

Mistake 1: Adding Context Without a Clear Task

Incorrect:

We use Java, Spring Boot, PostgreSQL, Redis, Docker, and AWS.

This is information, but it does not tell the model what to do.

Correct:

We use Java, Spring Boot, PostgreSQL, Redis, Docker, and AWS.
Design a caching strategy for the product catalogue API.

Mistake 2: Using Vague Context

Incorrect:

Our application is large and sometimes slow.

Correct:

The application processes approximately 3,000 requests per minute.
The product-search API response time increases from 200 milliseconds to 1.8 seconds during peak traffic.

Mistake 3: Mixing Context and Instructions

Incorrect:

The application uses PostgreSQL and explain the indexes and it has ten million records and make it beginner-friendly.

Correct:

Technical context:
The application uses PostgreSQL.
The main table contains approximately 10 million records.
Task:
Explain how indexes could improve query performance.
Audience:
The explanation is intended for beginner backend developers.

Mistake 4: Including Conflicting Context

Incorrect:

Write for complete beginners.
Assume the reader understands advanced distributed-system concepts.

Correct:

Write for intermediate backend developers who understand REST APIs and databases but are new to distributed systems.

Mistake 5: Hiding Critical Rules

Critical security, legal, or formatting rules should not be buried inside long paragraphs.

Incorrect:

Create a report based on the information below, and somewhere in the process remember that customer identities should not be included.

Correct:

Privacy constraint:
Do not include customer names, email addresses, account numbers, or other identifying information.

Mistake 6: Repeating the Same Context

Repeated information wastes tokens and may create inconsistencies.

State each fact once in the most appropriate section.

Mistake 7: Providing Raw Context Without Structure

Large blocks of unstructured information are difficult to interpret.

Use labels such as:

Background:
Business context:
Technical context:
Current situation:
Constraints:
Required output:

Advanced Context Techniques

Layered Context

Layered context moves from general information to specific information.

Domain context:
The application operates in online education.
Business context:
Revenue comes primarily from advertising.
User context:
Users are software developers preparing for interviews.
Technical context:
The website uses PHP, HTML, CSS, and JavaScript.
Situational context:
Returning visitor engagement is currently low.
Task:
Suggest features that increase repeat practice sessions.

Authoritative Context

When multiple sources exist, identify which source should control the answer.

Treat the provided product requirements as authoritative.
Use the meeting notes only as supporting context.
When the two sources conflict, follow the product requirements.

Negative Context

Negative context tells the model which assumptions are incorrect.

The application does not use microservices.
Users are not required to create accounts.
The solution must not depend on a paid AI API.
Do not assume that user progress is stored in a server-side database.

Conditional Context

Conditional context explains how behaviour should change under different conditions.

When the input contains code, provide a code review.
When the input contains only an error message, provide diagnostic questions.
When required information is missing, list the missing details instead of inventing them.

Persistent Context

Persistent context applies throughout a multi-turn workflow.

Throughout this conversation, assume that all code uses Java 21 and Spring Boot 3.
Use constructor injection in every example.
Do not use deprecated APIs.
Keep code comments on a single line.

Persistent context should be:

• Stable
• Relevant to multiple tasks
• Clearly stated
• Updated when assumptions change

Reusable Context Template

Role:
You are [role or expertise].
Background:
[Relevant history or project information]
Business context:
[Product, customer, objective, revenue model, resources]
User context:
[Audience, knowledge level, goal, preferences]
Technical context:
[Technology stack, architecture, environment, current implementation]
Situational context:
[Current event, issue, urgency, impact]
Domain context:
[Industry terminology, rules, standards, processes]
Primary task:
[One clear objective]
Input data:
[Content to process]
Constraints:
[Mandatory and prohibited behaviour]
Priorities:
[Most important decision criteria]
Output format:
[Required response structure]
Completion condition:
[Conditions that define a complete response]

Complete Practical Example

Weak Prompt

Suggest how to improve my tutorial website.

Improved Prompt

Role:
You are a product strategist specialising in technical education websites.
Background:
The website provides free Java, Python, and prompt engineering tutorials.
Each topic page contains a detailed article and interactive practice tools.
Business context:
The primary revenue source is display advertising.
The website is managed by one developer.
Paid APIs and expensive infrastructure should be avoided.
User context:
The main users are students and software developers preparing for interviews.
Many users access the website from mobile devices.
Technical context:
The website uses PHP, HTML, CSS, and JavaScript on shared hosting.
User registration is not currently required.
Situational context:
The website has useful content, but repeat visits and average session duration need improvement.
Primary task:
Recommend practical features that increase repeat visits, practice activity, and page engagement.
Constraints:
Do not recommend features that require a large development team.
Do not depend on paid third-party APIs.
Priorities:
Prioritise low-cost features that can be implemented incrementally.
Output format:
Present the recommendations in priority order.
For each recommendation, include user value, implementation difficulty, expected engagement impact, and required technology.
Completion condition:
Include at least ten distinct recommendations and identify the best three features to implement first.

Why This Prompt Works

The prompt provides:

• A clear expert role
• Relevant website background
• Business objectives
• User characteristics
• Technical limitations
• Current problem
• One primary task
• Explicit prohibitions
• Prioritisation criteria
• A measurable completion condition

The model does not need to guess the platform's purpose, resources, technology, audience, or business model.

Best Practices

1. Start with one clear task.
2. Add only context that affects the result.
3. Identify the intended audience.
4. Explain why the output is needed.
5. Include the current business or technical environment.
6. State important constraints explicitly.
7. Separate context from input data.
8. Place global context before the task.
9. Place local context near the related instruction.
10. Prioritise critical information.
11. Remove repeated or unrelated details.
12. Define ambiguous terminology.
13. Include measurable facts when available.
14. Mark assumptions instead of presenting them as facts.
15. Identify authoritative reference information.
16. Use headings or labels in complex prompts.
17. Split large tasks when context becomes difficult to manage.
18. Review the prompt for missing information.
19. Check for conflicting instructions.
20. Define what a complete response must contain.

Final Context Quality Checklist

Before using a prompt, confirm the following:

• The model knows what task it must perform.
• The model understands why the task is required.
• The relevant background is available.
• The intended user or audience is identified.
• The business environment is explained where necessary.
• The technical environment is explained where necessary.
• The immediate situation is clear.
• The correct domain is identified.
• Important terminology is defined.
• Relevant facts are included.
• Irrelevant details are removed.
• Critical context appears before supporting context.
• Context is grouped logically.
• Constraints are visible and specific.
• Conflicting information has been resolved.
• Assumptions are explicitly controlled.
• The expected output format is defined.
• The completion condition is measurable.
• The prompt does not contain unnecessary repetition.
• The context is sufficient without becoming overloaded.

Conclusion

Providing context is one of the most important principles of effective prompt design.

Instructions define what an AI model should do, while context explains the environment in which the task must be completed. Strong context helps the model understand the audience, objective, domain, technical environment, business requirements, current situation, and important limitations.

Effective context should be:

• Relevant
• Accurate
• Complete
• Structured
• Prioritised
• Concise
• Consistent with the task

Too little context causes ambiguity and incorrect assumptions. Too much context hides the objective and reduces focus. The goal is not to provide every available detail. The goal is to provide the smallest set of high-quality information required to produce an accurate, relevant, and actionable response.

A well-designed context-rich prompt gives the model a clear operating environment. This leads to responses that are more precise, more useful, and better aligned with the user's actual requirements.

Frequently Asked Questions