Generative AI projects are the most requested service category in Surat IT right now. They're also the most commonly failed. The gap between client expectations and actual AI capabilities is where budget gets burned and relationships end.
The oversell problem: when a client asks for "an AI chatbot," they often imagine a system that understands every question perfectly, never hallucinates, and works out of the box. The actual product requires careful prompt engineering, RAG architecture for accurate data retrieval, guardrails for safety, and ongoing monitoring. Clients rarely know this upfront.
The fix: start with a discovery sprint. Before writing a line of code, spend 2–3 days mapping: (1) What specific task are we automating? (2) What does "good" output look like? (3) What's the tolerance for error? A chatbot that's 90% accurate might be fine for internal FAQ — and catastrophic for medical advice.
Architecture decisions that matter: retrieval-augmented generation (RAG) is now the standard pattern for enterprise AI that needs to reference company data. If a client wants their AI to "know" their documentation, product catalog, or policies — RAG is non-negotiable. Pure prompt engineering without RAG produces unreliable results at scale.
Setting the evaluation framework before build: define your success metric before you start. "The AI should answer 85% of tier-1 support queries without human escalation" is measurable. "The AI should be helpful" is not. Clients who agree to evaluation criteria upfront are much easier to work with at delivery.
Hallucination management: every enterprise GenAI project needs a human-in-the-loop checkpoint for high-stakes outputs. Build this into your architecture from day one. Clients who experience hallucinations in production without a safety net become very angry former clients.
Pricing GenAI projects: don't price like a fixed-scope project. GenAI work requires iteration — the prompt that works in testing often needs adjustment in production. Use a phased model: discovery + MVP + refinement + monitoring. Monthly retainers for monitoring are both fair and good revenue.
"A chatbot that's 90% accurate might be fine for internal FAQ — and catastrophic for medical advice. Define success criteria before you write a line of code."
