---
title: RAG Implementation
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/rag-implementation/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/rag-implementation/
last_updated: 2026-06-20
---

# RAG Implementation

> Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases…

A production blueprint for Retrieval-Augmented Generation systems that ground LLM answers in your own documents instead of letting the model guess. It separates retrieval quality from generation quality so you can debug each layer independently, and ships with faithfulness-first prompting that forces the model to cite sources or say 'I don't have enough information' rather than hallucinate. The result is a knowledge assistant your users can actually trust.

## Use cases
- Document Q&A over proprietary knowledge bases
- Chatbots that answer from current, factual sources
- Natural-language semantic search
- Documentation assistants with source citations
- Research tools that show their references
- Reducing hallucinations in customer-facing AI

## Benefits
- Answers grounded in your sources with inline citations, not invented facts
- Independent debugging of retrieval vs generation, so you fix the real cause
- Lower token spend through context-budget management and contextual compression
- Measurable quality via precision, recall, and faithfulness metrics you can track in production

## What’s included
- LangGraph retrieve-then-generate pipeline ready to run
- Hybrid search (BM25 + dense embeddings) with Reciprocal Rank Fusion weighting
- Five advanced patterns: multi-query, contextual compression, parent-document retriever, HyDE, reranking
- Vector store configs for Pinecone, Weaviate, Chroma, and pgvector
- Chunking strategies (recursive, token-based, semantic, markdown header) with size/overlap guidance
- Structured-output responses with confidence score and source IDs, plus an evaluation harness

## Who it’s for
Engineering teams building knowledge-grounded AI assistants, Q&A systems, or semantic search over their own documents.

## How it runs
When a RAG answer is wrong, the first question is which layer failed. Retrieval and generation are measured separately, with hybrid search, reranking, and a weekly benchmark that catches silent drift.
1. Treats retrieval and generation as two separate problems with separate metrics: precision and recall for the retrieval side, faithfulness and relevance for the generation side. When quality drops, the logs isolate which layer failed instead of guessing at an end-to-end score.
2. Chunks by use case, not by habit: 256 to 512 tokens for precise Q&A, 1000 to 2000 for analysis and summarization, and the parent-document pattern to get both, small chunks for matching, large parents for context. Overlap stays in the 10 to 20 percent band.
3. Retrieves hybrid: BM25 keyword matching and dense embeddings fused with weighted rank fusion (typically 30 percent sparse, 70 percent semantic), pulling 20 to 50 candidates for high recall.
4. Reranks before generating: a cross-encoder or rerank API reorders the candidates down to the final top-k, with MMR available when diversity matters. Skipping this stage typically costs 15 to 25 points of precision.
5. Generates faithfulness-first: the prompt restricts answers to the provided context, citations are mandatory, and structured output carries a confidence score with an automatic 'not found in my sources' fallback below 0.5.
6. Evaluates continuously: a fixed test set scores retrieval precision, recall and answer faithfulness every sprint, the embedding model version lives in metadata so an upgrade triggers a full re-embed (partial mixing of vector spaces is banned), and a weekly 50-query benchmark catches silent drift.

## FAQ
### Does this work with my existing vector database, or do I have to switch?
It ships vector store configs for Pinecone, Weaviate, Chroma, and pgvector, so on any of those you mostly wire in credentials. A different store means adapting the config yourself; the retrieval pipeline and chunking strategies stay the same.

### We already embed documents and stuff the top chunks into the prompt, where does that naive setup fall short of this blueprint?
It separates retrieval quality from generation quality so you can debug each layer on its own, and runs hybrid search that fuses BM25 with dense embeddings via Reciprocal Rank Fusion. The faithfulness-first prompting then forces the model to cite sources or say it lacks information instead of guessing.

### Will it fine-tune or train a model on my documents?
No. This is retrieval, not training: your documents sit in a vector store and get pulled into context at query time. The model's weights never change, which is also why your content stays portable across models.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI for data analytics](https://forgehouse.ai/guides/ai-data-analytics/)