Tutorials Prompt Engineering Tutorial
Context Injection — Complete Guide
Context Injection — 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 46 of 100
Context Injection
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
Context injection is the step where retrieved chunks are placed into the prompt — order, labels, and delimiters matter.
Why should you care?
PromptVerse injects numbered [1][2] sources so the model and UI align on citations.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
const block = chunks.map((c,i) => `[${i+1}] doc_id=${c.id}\n${c.text}`).join("\n\n");
const user = `Sources:\n${block}\n\nQuestion: ${q}\nCite [n] for each claim.`;What happened?
- Numbered sources map to citation tags.
- doc_id in header helps humans verify in admin UI.
Practice next
- Format 3 chunks with [1][2][3] labels.
- Ask model to cite numbers.
- Reorder chunks — see if wrong cite appears.
- Put highest score chunk first.
- Cap injected chars per source.
Remember
Label every chunk. Sources before question. Delimiters reduce bleed.
Citation UI
User clicks [2] in answer.
Outcome: UI opens doc_id from chunk 2 metadata.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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