Context Compression

Learning Objectives

By the end of this session, you will be able to:

  • Understand what Context Compression is

  • Learn why context size matters in RAG systems

  • Understand token limitations in LLMs

  • Explore different context compression techniques

  • Learn how enterprise RAG systems optimize context

  • Reduce costs while improving answer quality

  • Design efficient retrieval pipelines

Introduction

In the previous session, we learned about Re-Ranking and how advanced RAG systems improve retrieval quality by selecting the most relevant documents before sending them to the LLM.

We explored:

  • Multi-stage retrieval

  • Cross-encoders

  • Relevance scoring

  • Enterprise retrieval architectures

However, another challenge appears after retrieval.

Imagine a system retrieves:

20 Documents

Each document contains:

500 Words

Total retrieved content:

10,000+ Words

Can we send all of that information to the LLM?

Usually not.

Modern LLMs have limits.

They cannot process unlimited amounts of information.

This challenge introduces an important concept:

Context Compression

Context Compression helps reduce retrieved information while preserving the most important details.

It is one of the key optimization techniques used in advanced RAG systems.

Why This Topic Matters

Imagine an enterprise assistant.

Question:

What are the company's policies regarding remote work, travel reimbursement, security requirements, and international work arrangements?

The retrieval system finds:

Remote Work Policy

Travel Policy

Security Policy

Compliance Guidelines

International Work Guide

Together these documents may contain thousands of words.

Sending everything to the LLM can cause:

  • Higher costs

  • Slower responses

  • Increased noise

  • Reduced answer quality

Context compression helps solve these problems.

Understanding Context

In an LLM system, context refers to the information provided to the model before it generates a response.

Example:

User Question
      +
Retrieved Documents
      ?
Prompt

Everything inside the prompt becomes context.

The quality of this context directly affects answer quality.

What Is Context Compression?

Context Compression is the process of reducing the size of retrieved information while preserving the most relevant content.

Instead of:

10 Documents
      ?
Send Everything

the system performs:

10 Documents
      ?
Extract Important Parts
      ?
Smaller Context

The LLM receives only the information it needs.

Why Context Compression Is Necessary

Modern RAG systems face several challenges.

Token Limits

LLMs have context windows.

Cost

More tokens mean higher costs.

Latency

Larger prompts take longer to process.

Noise

Irrelevant information reduces answer quality.

Context compression addresses all four challenges.

Understanding Context Windows

Every LLM has a maximum context size.

A context window determines:

How Much Information
The Model Can Process

Example:

Question

Instructions

Retrieved Documents

Conversation History

must all fit inside the available context window.

This makes efficient context management important.

Simple Example

Suppose a retrieved document contains:

The company was founded in 2008.

The company has offices in 20 countries.

Employees may work remotely up to three days per week.

The company sponsors community events.

Question:

What is the remote work policy?

Only one sentence is relevant.

Compressed version:

Employees may work remotely up to three days per week.

This is a simple example of context compression.

Context Compression Workflow

Question
      ?
Retrieval
      ?
Documents
      ?
Compression
      ?
Relevant Context
      ?
LLM
      ?
Answer

The compression stage filters out unnecessary information.

Benefits of Context Compression

Lower Cost

Fewer tokens are processed.

Faster Responses

Smaller prompts execute faster.

Better Accuracy

Noise is reduced.

Improved Scalability

Large knowledge bases become manageable.

These benefits make context compression a core RAG technique.

Compression Technique 1: Extractive Compression

This is the simplest approach.

The system extracts only relevant sections.

Original Document:

1000 Words

Relevant Content:

100 Words

Only the useful information is passed to the model.

Example of Extractive Compression

Question:

What are the scholarship eligibility requirements?

Document:

Introduction

History

Eligibility Criteria

Application Process

Contact Information

Compressed Context:

Eligibility Criteria

The unnecessary sections are removed.

Compression Technique 2: Summarization

Instead of extracting text directly, the system creates a summary.

Original Content:

2000 Words

Summary:

200 Words

The key ideas are preserved.

This is useful for very large documents.

Example of Summarization

Original:

10-page Remote Work Policy

Compressed:

Employees may work remotely up to three days per week, subject to manager approval.

The essential information remains.

Compression Technique 3: Query-Aware Compression

This is one of the most effective approaches.

The system uses the user's question to determine what information should be retained.

Question:

What is the travel reimbursement policy?

Keep:

Travel Expenses

Remove:

Leave Policy

Security Guidelines

Benefits Information

Compression becomes personalized to the query.

Query-Aware Workflow

Question
      ?
Analyze Intent
      ?
Select Relevant Content
      ?
Compressed Context

Many modern RAG systems use this approach.

Compression Technique 4: Metadata-Based Compression

Metadata helps identify useful content.

Example:

Question:

Current travel policy

Metadata:

Latest Version

The system keeps:

Newest Policy

and removes outdated versions.

This reduces unnecessary context.

Real-World Example: University Assistant

Question:

What scholarships are available for MCA students?

Retrieved Documents:

Scholarship Policy

Student Handbook

MCA Program Guide

Hostel Information

Compression keeps:

Scholarship Eligibility

MCA Requirements

and removes unrelated content.

The answer becomes more accurate.

Real-World Example: HR Assistant

Question:

Can I work remotely from another city?

Retrieved Documents:

Remote Work Policy

Travel Policy

Benefits Guide

Compression extracts only:

Remote Work Rules

The LLM receives focused context.

Context Compression in Enterprise RAG

Enterprise systems often retrieve:

50+
Documents

Sending everything to the LLM is impractical.

Instead:

Retrieve
      ?
Re-Rank
      ?
Compress
      ?
Generate

This architecture improves both performance and cost efficiency.

Multi-Stage Context Pipeline

Modern systems often use:

Question
      ?
Hybrid Search
      ?
Top 50 Documents
      ?
Re-Ranking
      ?
Top 10 Documents
      ?
Compression
      ?
Final Context
      ?
LLM

This is a common production architecture.

Challenges in Context Compression

Over Compression

Important information may be removed.

Under Compression

Too much information remains.

Loss of Meaning

Summaries may omit details.

Additional Processing

Compression introduces extra computation.

Balancing these factors is important.

Measuring Compression Quality

Organizations often evaluate:

Context Size

How many tokens remain?

Retrieval Accuracy

Was relevant information preserved?

Answer Quality

Did responses improve?

Cost Reduction

How many tokens were saved?

These metrics help optimize compression strategies.

Compression and Cost Optimization

Consider:

Without Compression:

10,000 Tokens

With Compression:

2,000 Tokens

Result:

Lower Cost
Faster Response

This can significantly reduce operational expenses.

Enterprise Use Cases

Knowledge Assistants

Large policy repositories.

Legal Systems

Contract analysis.

Healthcare Systems

Clinical guidelines.

Research Assistants

Scientific paper retrieval.

Customer Support

Large documentation libraries.

These systems commonly use context compression.

Future of Context Compression

Industry trends include:

AI-Driven Compression

LLMs compressing content automatically.

Dynamic Compression

Compression adjusted to query complexity.

Personalized Compression

User-specific context selection.

Agentic Compression

AI agents deciding what information to keep.

These advancements will further improve RAG efficiency.

Enterprise Architecture

Knowledge Sources
         ?
Retrieval
         ?
Re-Ranking
         ?
Compression
         ?
Context Builder
         ?
LLM
         ?
Answer

This architecture is increasingly common in enterprise AI systems.

.NET Perspective

Common technologies include:

  • Semantic Kernel

  • Azure OpenAI

  • Azure AI Search

  • ASP.NET Core

These tools support context management and prompt optimization.

Python Perspective

Popular frameworks include:

  • LangChain

  • LlamaIndex

  • Haystack

  • OpenAI SDK

These ecosystems provide built-in support for context compression strategies.

Assignment

Design Exercise

Design a context compression pipeline for:

University Knowledge Assistant

Include:

  • Retrieval

  • Re-Ranking

  • Compression

  • Answer Generation

Explain how compression improves performance.

Research Activity

Compare:

  • Extractive Compression

  • Summarization

  • Query-Aware Compression

Analyze:

  • Accuracy

  • Cost

  • Complexity

  • Enterprise Suitability

Key Takeaways

  • Context Compression reduces the amount of information sent to an LLM.

  • It helps manage token limits, costs, and latency.

  • Extractive compression removes irrelevant content.

  • Summarization creates shorter representations of documents.

  • Query-aware compression keeps only information relevant to the user's question.

  • Modern enterprise RAG systems commonly include a compression stage.

  • Effective compression improves both performance and answer quality.

What's Next?

In Session 36, we will explore:

Query Transformation

You will learn how advanced RAG systems rewrite user questions, improve retrieval quality, expand ambiguous queries, and use AI-powered query optimization techniques before retrieval begins.