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Frequently asked questions

Should we use RAG or fine-tuning for an internal LLM assistant?

RAG usually fits questions grounded in changing internal documents, especially when users need citations and permission-aware retrieval. Fine-tuning can help with consistent behavior, formatting, or specialized patterns, but is not a reliable substitute for current source material. Start with an evaluation set, then determine whether failures come from retrieval, instructions, or model capability.

How do LLM developers reduce hallucinations in production?

No implementation can guarantee hallucination-free answers. Developers can improve grounding with better retrieval, source citations, structured outputs, and explicit abstention rules. Evaluation should separate retrieval failures from unsupported generation and include unanswerable questions. Schema validation checks output structure, not truth, so factual claims still need comparison against trusted source material.

Do we need LangChain or LangGraph to build an LLM application?

Not necessarily. A straightforward assistant may only need a model SDK, retrieval code, and ordinary application services. LangGraph can help when workflows require explicit state, branching, checkpoints, or human approval. Choose a framework when its abstractions solve a concrete orchestration problem, and avoid adding dependencies that make debugging model calls harder.

How can we protect private data in a RAG application?

Enforce authorization during retrieval, not just in the chat interface. Keep document permissions attached to indexed content and test for cross-user information exposure. Review model-provider retention settings, logging, secret handling, and tool permissions. Treat retrieved documents as untrusted input, and require application-side checks before any model-requested action reaches a sensitive system.

What should an LLM developer evaluate before we launch?

Build a representative test set covering correct answers, missing evidence, conflicting documents, prompt injection, and tool failures. Track retrieval relevance, groundedness, task completion, and latency separately. Tools such as LangSmith or Phoenix can support tracing and evaluation, but domain reviewers still need clear rubrics. Repeat evaluations when prompts, models, or indexed content change.

Are your developers really based in the US?

Yes. Every engineer lives and works in the United States. No offshore subcontracting and no hand-offs to another team. That's written into the contract.

How fast can someone start?

Most teams interview matched engineers within a week of the first call. Start dates depend on your interview process.