Tutorials Prompt Engineering Tutorial
AI Context Management — Complete Guide
AI Context Management — 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 19 of 100
AI Context Management
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 2: Basic Prompting Techniques
What is this?
Context management decides what text reaches the model this turn: system rules, retrieved chunks, chat history, and tool results — within token budget.
Why should you care?
PromptVerse Context Manager ranks, dedupes, and truncates sources before the prompt is built.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
function buildContext({ system, history, chunks, maxTokens }) {
const ranked = dedupeByDocId(chunks).slice(0, 5);
const trimmedHistory = summarizeTurns(history, 800);
return pack(system, trimmedHistory, ranked, maxTokens);
}What happened?
- Deduping avoids repeating the same policy chunk.
- Summarized history frees tokens for fresh retrieval on long chats.
Practice next
- List all context sources your app uses.
- Assign token budget per source.
- Drop lowest-scored chunk first when over budget.
- Pin system + latest user message as non-droppable.
- Try recency vs relevance ranking.
Remember
Rank and trim before LLM call. History summarization saves window. Dedupe retrieved chunks.
Long copilot session
Developer chats 40 turns about one bug.
Outcome: Rolling summary keeps fix context; old small talk drops.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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