RAG Development Services That Ground LLMs in Your Enterprise Data

Frequently Asked Questions

• What is RAG (Retrieval-Augmented Generation) and why do enterprises use it?

  RAG is an architecture that pairs a large language model with a retrieval system over your own data. At query time, the system fetches relevant chunks from your knowledge base, passes them to the LLM as context, and generates an answer grounded in your sources. Enterprises use RAG to make LLMs accurate on proprietary data, reduce hallucinations, enable citation-backed responses for regulated use cases, and avoid the cost and complexity of fine-tuning large foundation models.

• How is RAG different from fine-tuning a model?

  Fine-tuning modifies a model’s weights to teach it a domain or behavior. RAG keeps the model as-is and instead retrieves relevant context at query time. RAG is faster to iterate (no retraining), cheaper at low-to-moderate scale, keeps knowledge current (just re-index), and avoids catastrophic forgetting. Fine-tuning wins when you need domain vocabulary, consistent tone, or low-latency inference at massive scale. Most production systems use both , fine-tuned models inside RAG pipelines.

• What is GraphRAG and when should we use it over vector-based RAG?

  GraphRAG retrieves from a knowledge graph (entities + relationships) instead of , or in addition to , a vector store. Use it when answers require multi-hop reasoning (e.g., “which regulations affect this contract given this counterparty’s jurisdiction?”), when your domain is inherently relational (legal precedents, org hierarchies, drug interactions), or when chunk-level retrieval loses context that only entity relationships preserve. GraphRAG is often hybridized with vector search for best results.

• How much does it cost to build an enterprise RAG system?

  Discovery and architecture sprints start at $15k-$40k. A production pilot on one use case typically runs $60k-$150k over 6 to 10 weeks. A full enterprise-scale RAG platform ranges $200k-$800k+ depending on number of knowledge sources, compliance requirements, and expected query volume. Ongoing run costs (embedding, vector storage, generation, reranking, observability) typically range $0.005-$0.05 per query at scale, with heavy optimization opportunities as volume grows.

• Which vector database should we use , Pinecone, Weaviate, Qdrant, or pgvector?

  No single answer , it depends on scale, sovereignty, hybrid search needs, and existing stack. Pinecone is the lowest-operations managed option for teams that want zero infra work. Weaviate offers strong hybrid search and self-hosting flexibility. Qdrant is excellent for self-hosted deployments with rich filtering. pgvector on PostgreSQL is ideal when your data is already in Postgres and query volume is moderate. Milvus handles the largest deployments. We benchmark your query patterns across candidates before recommending.

• How do you prevent hallucinations in production RAG systems?

  Hallucination prevention is a layered defense: (1) strong retrieval quality via hybrid search + reranking so the LLM gets the right context, (2) prompt engineering that requires citations and permits “I don’t know” refusals, (3) entailment checks that validate every claim against retrieved context, (4) groundedness scoring via RAGAS or LLM-as-judge, (5) context compression to remove noise, and (6) production monitoring that flags low-confidence responses. No single layer is sufficient , hallucination is a systems problem.

• Can RAG be deployed on-premises or air-gapped for regulated data?

  Yes. We routinely deploy RAG in private cloud, on-premises, and air-gapped environments using open-source embedding and generation models (Llama, Mistral, Qwen, bge-m3), self-hosted vector stores (Weaviate, Qdrant, Milvus, pgvector), and internal observability stacks. All components can run without any egress to third-party APIs. This is standard for financial services, healthcare, defense, and government customers.

• How do you evaluate RAG quality , what does "good" look like?

  We measure four core dimensions: (1) context precision , what fraction of retrieved chunks are relevant; (2) context recall , what fraction of needed information was retrieved; (3) faithfulness , do answers stay grounded in retrieved context without fabrication; (4) answer relevance , does the response address the actual question asked. Tools: RAGAS, TruLens, DeepEval, LangSmith, and custom LLM-as-judge evaluators. A production-quality RAG system typically scores >0.85 on faithfulness and >0.80 on context precision against a domain-specific golden set.

• What is agentic RAG and how does it differ from standard RAG?

  Standard RAG follows a fixed retrieve-then-generate flow. Agentic RAG gives an LLM agent the ability to plan retrieval: decide whether retrieval is needed, which tools to call, how to decompose complex questions, when to re-query with better parameters, and when to stop. It uses function calling, MCP (Model Context Protocol), and ReAct-style reasoning loops. Agentic RAG shines on open-ended research, multi-source synthesis, and workflows that must escalate to humans under uncertainty. It is more expensive per query but dramatically better on complex tasks.

• How long does it take to go from idea to production RAG?

  A focused, single-use-case RAG pilot typically reaches production in 8 to 12 weeks: 2 weeks discovery and architecture, 4 to 6 weeks build, 2 weeks evaluation and hardening. Enterprise-scale platforms with multiple knowledge sources, compliance certification, and multi-tenant architecture typically run 4 to 6 months end-to-end. The highest-velocity teams we’ve worked with moved from kickoff to first business value (not full rollout) in 5 weeks by scoping tightly and accepting a v1 that intentionally excluded the long tail.