Tutorials Prompt Engineering Tutorial
Context Compression — Complete Guide
Context Compression — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 82 of 100
Context Compression
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
Context compression summarizes or extracts key facts from long text before injection — fitting more meaning in fewer tokens.
Why should you care?
PromptVerse compresses 10 retrieved chunks into bullet facts before the answer prompt.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
raw_chunks = retrieve(q, k=10) # 8k tokens
compressed = llm("Extract only facts relevant to Q as bullets, no fluff", { q, raw_chunks }, max_tokens=400)
answer = llm(answer_prompt(q, compressed))What happened?
- First LLM call distillates retrieval.
- Second call answers from 400-token fact sheet — cheaper than 8k in answer prompt.
Practice next
- Take one long doc.
- Summarize to 10 bullets for a specific question.
- Answer from bullets only.
- Map compress bullets back to doc_id.
- Skip compress when total chunks < 1k tokens.
Remember
Compress retrieval before answer step. Keep numbers and dates in compress prompt. Two-hop costs less than huge single prompt.
Long policy doc
50-page handbook in RAG.
Outcome: Compress hop fits window; answers stay grounded.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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