Wiring Retrieval Into the Prompt
Answer with grounded, cited context.
Wiring Retrieval Into the Prompt is a free NLP Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Putting the Pieces Together
You can retrieve relevant chunks and call an LLM. Now you connect them: feed the retrieved text into the prompt as context.
The Augmented Prompt
A RAG prompt has three parts: an instruction, the retrieved context, and the user question. The model reads all three together.
Joining the Retrieved Chunks
First stitch your top chunks into one block of text. A blank line between them keeps each passage readable to the model.
context = "\n\n".join(hits)Building the Prompt String
Wrap the context and question in clear labels. A simple template tells the model exactly what to use and what to answer.
prompt = f"Context:\n{context}\n\nQuestion: {question}\nAnswer:"Telling the Model to Stay Grounded
Add an instruction to answer only from the context. This curbs hallucination and keeps the response tied to your sources.
Handling Unknown Answers
Tell the model to say it does not know when the context is silent. Allowing I do not know beats a confident wrong guess.
Sending It to the LLM
Pass the assembled prompt to your model call. The LLM now answers using your retrieved context, not just its training.
answer = llm(prompt)Showing the Sources
Return the chunks you used alongside the answer. These citations let users verify the claim and build trust.
Watch the Context Length
Too many chunks overflow the context window. Keep k small and trim long passages so the prompt fits the model budget.
A Minimal RAG Function
The full flow is tiny: embed, search, build prompt, generate. This one function captures an entire RAG pipeline.
def rag(q):
hits = retrieve(q)
return llm(build_prompt(hits, q))Improving Retrieval Quality
If answers are weak, the fix is usually retrieval, not the LLM. Better chunks and re-ranking raise quality more than a bigger model.
Quick Check
Think about what goes into a RAG prompt.
Recap
You join retrieved chunks into context, build an instruction-plus-question prompt, generate a grounded answer, and show sources. ✅
Frequently asked questions
Is the “Wiring Retrieval Into the Prompt” lesson free?
Yes — the full text of “Wiring Retrieval Into the Prompt” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Wiring Retrieval Into the Prompt”?
Answer with grounded, cited context. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Wiring Retrieval Into the Prompt” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Why LLMs Need Retrieval
- Chunking and Embedding Documents
- Vector Search With a Vector Store
- Wiring Retrieval Into the Prompt