LLM & Generative AI Consulting

Default to RAG. Fine-tuning is for style/format adaptation; RAG is for factual grounding in your data. Most enterprise use cases — internal search, customer support, document Q&A — are RAG. Fine-tune only when you've validated RAG and still need behavior the base model can't deliver.

Depends on cost, latency, data residency, and quality. We start with Anthropic Claude or GPT-class models for prototyping (fastest path to validate the use case), then evaluate switching to open-source (Llama, Qwen) only when we have evidence the cost or compliance pressure justifies it. Self-hosting open models is genuinely expensive — don't pick it for ideology.

Several layers: chunking strategy tuned to your data shape (not generic 512-token windows), hybrid retrieval (BM25 + vector) over pure semantic, re-rankers for top-N, citation requirements in the prompt, and a deterministic eval harness that runs on every change. Hallucination rate is a measured metric, not a vibe.

Input filtering, structured output (function calling / JSON mode rather than free text), separation between trusted and untrusted text in the prompt template, output validation, and a clear privilege model (the LLM never has direct access to write APIs — always through validated tool calls). We assume injection attempts will happen and design for them.

Use enterprise tiers (Anthropic, OpenAI Enterprise, Azure OpenAI) with data-not-trained guarantees. For genuinely sensitive data, deploy a self-hosted open model in your VPC. We've shipped both patterns. The decision is usually compliance- or contract-driven, not technical.

First production feature in 6–12 weeks for a focused use case. Realistic ROI window: 3–6 months for adoption + measurable impact. Anyone promising shorter is either selling magic or not measuring rigorously. We push back hard on demos that ignore the operations cost of running LLMs in production.