Beginner LevelAI, LLMs, Embeddings & MCP
What is Retrieval-Augmented Generation (RAG) and how does it prevent LLM hallucinations?
❓ Interview Prompt: Explain the 3 phases of a RAG pipeline (Ingestion, Retrieval, Generation) and ground truth context injection.
Retrieval-Augmented Generation (RAG) enhances LLM responses by dynamically injecting relevant domain document context into the model's prompt before text generation occurs.
Three Pipeline Phases:
1
Ingestion Phase: Documents are chunked into text segments, converted into vector embeddings, and stored in a vector database.
2
Retrieval Phase: When a user submits a query, the system computes the query embedding and retrieves top-K semantically relevant document chunks.
3
Generation Phase: Retrieved document chunks are formatted into a system prompt as ground truth context for the LLM to answer accurately.
Primary Advantage:
Grounds model generation in verifiable static documents without expensive model retraining, drastically reducing hallucinations.
Code Implementation Example
typescript
async function generateRAGAnswer(userQuery: string): Promise<string> {
// 1. Embed query & retrieve top 3 context chunks
const queryVector = await embedText(userQuery);
const retrievedChunks = await vectorStore.query({ vector: queryVector, topK: 3 });
const contextText = retrievedChunks.map(c => c.text).join('\n---\n');
// 2. Inject context into LLM prompt
const prompt = "Context information is below:\n" + contextText + "\n\nGiven the context above, answer the question: " + userQuery;
return await llmClient.generate(prompt);
}💡Basic RAG pipeline combining vector retrieval with LLM prompt context injection.
🎯 Interview Key Takeaway
RAG prevents hallucinations by dynamically retrieving domain document context and injecting it into the LLM prompt.
#RAG#Vector Search#Prompting#Beginner
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