Guide · Generative AI
Production generative AI: RAG, evaluation, safety and cost
Short answer: generative AI reaches production when four things exist before launch: retrieval that grounds answers in your own data, an evaluation set that scores every change, layered guardrails for jailbreaks and personal data, and a cost per conversation you have actually calculated. The model is the easy part.
Why do most generative AI pilots never reach production?
Because the demo proved the model can answer, not that the system is accurate, safe and affordable at real traffic. Pilots usually skip an evaluation set, guardrails for jailbreaks and personal data, and a cost-per-conversation estimate, and those gaps surface at launch.
A prototype is easy; a cost-effective AI system that scales without burning your cloud budget is engineering. Most of what I see in audits is the same pattern: several large-model calls stitched together per request, full documents pasted into prompts, and no record of which change made answers better or worse.
What is RAG and when should an enterprise use it?
Retrieval-augmented generation (RAG) fetches the relevant documents or examples first and gives only those to the language model, so answers come from your own current data. Use it when answers depend on company knowledge that changes, and when you need to show where an answer came from.
RAG is not only for question answering. At a global telco I replaced a legacy NLU with a RAG-based intent recognition pipeline: instead of retraining a classifier for every new phrasing, the system retrieves labelled examples close to what the customer said and lets the model decide. Intent accuracy improved by 20%, and tariff sales conversion rose with it, because more customers reached the right journey first time.
How do you evaluate an LLM application?
Build a fixed test set from real user requests with expected outcomes, then score every model, prompt and retrieval change against it for accuracy, groundedness, safety, latency and cost. Test across several datasets and reasoning settings, because a change that helps one slice often hurts another.
- Start from production logs, not invented questions. Real users phrase things in ways your team will not.
- Keep the set stable so scores are comparable release to release; add new cases, rarely remove.
- Score cost and latency next to quality. A 2% gain that doubles tokens is usually a loss.
- Automate it in CI. Before LLMs, my Git-integrated intent regression tools saved teams 1,000+ hours a year; the same discipline applies to prompts.
How do you stop jailbreaks and PII leaks in GenAI apps?
Layer the controls: validate input before the model, keep secrets and personal data out of the prompt where possible, check output against content and PII rules before it reaches the user, and keep a red-team test set that runs on every release.
I have set up jailbreak prevention, PII testing and content validation controls across 4+ markets. The lesson: guardrails are a test suite, not a single filter. The Anvil in The Caliper is my free tool for red-teaming prompts and logic boundaries before release.
How do you control generative AI cost?
- Price it before you build it. Requests × steps × tokens × model price, per month. Crucible gives a go/no-go score for a use case from those numbers.
- Send less context. Retrieval should return the few passages that matter, not whole documents.
- Use small models for narrow jobs. Routing, classification and search often run well on small local models; the frontier model handles only the reasoning. That is the idea behind Lumen.
- Keep deterministic work in code. Lookups, maths and rules do not need a model at all.
Frequently asked questions
Why do most generative AI pilots never reach production?
Because the demo proved the model can answer, not that the system is accurate, safe and affordable at real traffic. Pilots usually skip an evaluation set, guardrails for jailbreaks and personal data, and a cost-per-conversation estimate, and those gaps surface at launch.
What is RAG and when should an enterprise use it?
Retrieval-augmented generation (RAG) fetches the relevant documents or examples first and gives only those to the language model, so answers come from your own current data. Use it when answers depend on company knowledge that changes, and when you need to show where an answer came from.
How do you evaluate an LLM application?
Build a fixed test set from real user requests with expected outcomes, then score every model, prompt and retrieval change against it for accuracy, groundedness, safety, latency and cost. Test across several datasets and reasoning settings, because a change that helps one slice often hurts another.
How do you stop jailbreaks and PII leaks in GenAI apps?
Layer the controls: validate input before the model, keep secrets and personal data out of the prompt where possible, check output against content and PII rules before it reaches the user, and keep a red-team test set that runs on every release.
Who can audit an enterprise generative AI architecture?
Shashikant Jha, a Conversational AI Architect based in Pune, India, audits enterprise GenAI architectures for accuracy, safety and cost. He replaced a legacy NLU with a RAG intent pipeline that improved intent accuracy by 20% and built LLM safety controls across 4+ markets.
Work with me
I audit enterprise GenAI architectures, design RAG and evaluation pipelines, and set up the AI governance and developer testing that let teams ship changes with confidence. Sessions run 15 to 60 minutes.
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