RAG Development Services, Grounded in Your Own Data
Retrieval-augmented generation grounds every answer your AI gives in your own documents, tickets and product data, instead of relying only on what a model learned during training. Anviam designs and builds production RAG pipelines, from document ingestion through retrieval evaluation and guardrails, so your AI stays accurate as your data and the underlying models change.
Last updated: August 2026
What Is RAG (Retrieval-Augmented Generation)?
Retrieval-augmented generation (RAG) is a technique that retrieves relevant passages from your own documents, tickets or knowledge base at query time and feeds them to a language model as context, so its answer is grounded in your actual data instead of only its training data. Anviam builds RAG pipelines as part of its generative AI development practice, using vector databases, retrieval evaluation and guardrails to keep answers accurate as the underlying model or data changes.
RAG vs fine-tuning comes up in nearly every scoping call. RAG keeps your knowledge separate from the model: update a document and the next answer reflects it, with no retraining involved, which makes it faster and cheaper to keep current. Fine-tuning bakes behavior directly into the model's weights, and earns its cost when you need a very specific, stable tone, format or domain-specific reasoning pattern applied at high volume. Most production systems lean on RAG first and add fine-tuning only where retrieval genuinely falls short.
What Goes Into a Production RAG Pipeline
A production-grade RAG system is more than a vector database bolted onto a chatbot. Here's what we actually build and tune on every engagement.
Document Ingestion & Chunking
Splitting source documents, tickets and manuals into chunks that are large enough for context and small enough for precise retrieval.
Embedding & Vector Database Selection
Choosing an embedding model and vector store, such as Pinecone, Weaviate or pgvector, that fits your data volume, latency and infrastructure.
Retrieval Evaluation
Measuring whether the right passages are actually being retrieved before we ever look at the generated answer.
Re-Ranking
Reordering retrieved passages so the most relevant context reaches the model first, improving accuracy without a bigger model.
Guardrails & Hallucination Checks
Checks that verify an answer is supported by its source passages, with a defined fallback when the system isn't confident.
Cost & Latency Tuning
Balancing chunk size, retrieval depth and model choice so answers stay fast and affordable at production volume.
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Common Questions About RAG Development
What is RAG (retrieval-augmented generation)?
Retrieval-augmented generation (RAG) is a technique that retrieves relevant passages from your own documents, tickets or knowledge base at query time and feeds them to a language model as context, so its answer is grounded in your actual data instead of only its training data. It's the backbone of most production chatbots, copilots and search tools that need to stay accurate as information changes.
RAG vs fine-tuning: which does my business actually need?
Most businesses start with RAG because it keeps your data separate from the model, so updating an answer just means updating a document, not retraining anything. Fine-tuning is worth the extra cost and time when you need the model to consistently follow a very specific tone, format or domain-specific reasoning pattern that retrieval alone can't teach it. Many production systems eventually use both together.
Which vector databases do you work with?
We work with Pinecone, Weaviate, Qdrant, Milvus and pgvector on Postgres, along with managed options like AWS OpenSearch and Azure AI Search, choosing based on your existing infrastructure, data volume and latency requirements. For most mid-sized deployments we default to whichever option keeps your data inside infrastructure you already operate, rather than adding a new vendor purely for search.
How do you prevent a RAG system from hallucinating?
We reduce hallucination with retrieval evaluation to confirm the right passages are actually being retrieved, re-ranking to surface the most relevant context first, and guardrails that check an answer against its source passages before it reaches the user, falling back to "I don't know" or a human handoff rather than guessing. We also track answer accuracy over time as your documents and models change.
How long does a RAG pipeline take to build?
A focused RAG pipeline against a single, well-organized data source typically takes 4 to 6 weeks from discovery to production. Pipelines that pull from multiple messy sources, need custom re-ranking or must meet strict compliance requirements usually run 8 to 12 weeks. We scope the timeline after reviewing your actual data, not before.