RAG Implementation: Make Your Data Answer Questions
AI that answers questions using your documents, knowledge base, and internal data. From architecture to production.
What is RAG?
RAG (Retrieval-Augmented Generation) makes AI answer from your documents, not training data.
The user asks a question. The system finds relevant chunks in your documents. The LLM generates an answer using those chunks as context. No hallucinations. Verifiable answers with sources.
RAG vs Fine-Tuning
Complementary, not opposing. Most production systems use both.
| Criteria | RAG | Fine-Tuning |
|---|---|---|
| Best For | Factual Q&A, document search, knowledge bases | Style, tone, domain-specific language |
| How It Works | Searches for relevant chunks at query time | Trains the model on your data |
| Cost | Custom pricing based on scope | Custom pricing + ongoing inference cost |
| Maintenance | Update documents, re-embed periodically | Re-train with new data |
| Accuracy | 85-95% for well-structured documents | High for behavior, low for new facts |
Implementation Steps
Raw data to working AI. 4 phases.
Data Prep
Document ingestión and processing. PDFs, HTML, databases, APIs. Cleaning and chunking that works.
Embedding
Convert chunks to vectors. Store in vector database.
Retrieval
Semantic search with hybrid filters (vector + keyword). Re-ranking. Quality monitoring.
Generation
LLM generates answers from retrieved context. Prompt engineering. Source citations. Continuous feedback.
Vector Database Comparison
The right database for your use case.
| Feature | Pinecone | Weaviate | Qdrant | FAISS |
|---|---|---|---|---|
| Type | Managed | Open-source | Open-source | Local library |
| Cost | Scales with usage | Free (self-host) | Free (self-host) | Free |
| Best For | Production, no infra | Complex queries | Real-time, high performance | Prototyping, small data |
| Scalability | Automatic | Manual or cloud | Manual or cloud | Limited to RAM |
How We Work
- Analysis of your data sources and use cases
- Vector database selection
- Embedding pipeline
- Hybrid retrieval (vector + keyword)
- Prompt engineering
- Production monitoring
- Integration with your existing stack
- Documentation and training
Squish: RAG Infrastructure
Squish is our RAG infrastructure platform. It handles embeddings, indices, and retrieval. We build RAG systems that ship in weeks.
- Managed vector indices
- Hybrid retrieval
- Quality monitoring
- Auto re-embedding when documents change
Pricing
RAG implementation. No hidden costs.
Simple RAG
Single document type, basic queries, one embedding model.
- 1 data source
- Basic chunking
- 1 embedding model
- Natural language queries
Complex RAG
Multiple data sources, advanced retrieval, production monitoring.
- Multiple data sources
- Hybrid retrieval
- Re-ranking and optimization
- Production monitoring
Enterprise RAG
Multi-tenant, real-time sync, compliance.
- Multi-tenant
- Real-time sync
- Compliance and audit
- SLA and dedicated support
FAQ
What is RAG and how does it work?
RAG (Retrieval-Augmented Generation) combines search with language models. When a user asks a question, the system searches your documents for relevant chunks, then the LLM generates an answer with that context. Your actual data, not training data.
How do I implement RAG with my own documents?
Steps: 1) Document ingestión (PDF, Word, HTML, databases). 2) Chunking (split documents into searchable pieces). 3) Embedding (convert chunks to vectors). 4) Vector storage (Pinecone, Weaviate, Qdrant, or FAISS). 5) Retrieval (find relevant chunks). 6) Generation (LLM answers with retrieved context).
Which vector database should I use?
Pinecone: managed, scales well, costs scale with usage. Weaviate: open-source, flexible, good for complex queries. Qdrant: open-source, high performance, good for real-time. FAISS: local, free, best for prototyping or small datasets. We choose based on your scale, budget, and team.
What is the difference between RAG and fine-tuning?
RAG: adds your data to the context window at query time. Best for factual Q&A, document search, knowledge bases. Fine-tuning: trains the model on your data. Best for style, tone, domain-specific language. Most systems use RAG for knowledge and fine-tuning for behavior.
How much does RAG implementation cost?
Cost depends on system complexity. Simple RAG with a single document type and basic queries is a smaller engagement. Complex RAG with multiple data sources and advanced retrieval requires more investment. Enterprise RAG with multi-tenant and compliance are enterprise projects.
How long does RAG implementation take?
MVP (basic RAG, single data source): 2-4 weeks. Production system (multiple sources, monitoring, optimization): 4-8 weeks. Enterprise deployment (compliance, multi-tenant, real-time): 8-16 weeks.
How accurate is RAG?
Accuracy depends on: document quality, chunking strategy, embedding model, retrieval quality, and prompt engineering. We typically get 85-95% accuracy for well-structured documents. We measure precisión/recall throughout the project.
Can RAG work with Spanish documents?
Yes. Modern embedding models (OpenAI, Cohere, multilingual models) handle Spanish natively. We build bilingual RAG systems that retrieve and generate in the language of the query or document.
Related Resources
Guides and analysis for implementing RAG and integrating LLMs.
Build RAG with your data?
Book an architecture review. We analyze your data and give you a plan.
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