FAQs: GenAI Engineering

OpenAI, Anthropic (Claude), and open-source models via providers like Together or self-hosted where it makes sense.

Yes — retrieval-augmented generation with vector databases (Pinecone, pgvector, Weaviate) is a core part of the work.

Grounded retrieval, structured outputs, automated evals, and human-review checkpoints for anything customer-facing.

Either — I can own the full feature end-to-end, or plug into your existing engineering team as the GenAI specialist.

A single production-ready feature — a RAG system or an AI-assisted workflow, for example — typically runs ₹2–4L depending on complexity and how much evaluation tooling is needed. Ongoing iteration after launch is priced as a lighter monthly retainer.

A full-time AI engineer is a multi-month hiring process for a skill set that’s still hard to interview for reliably. This gets a production-hardened feature shipped in weeks, and I can hand off documentation and evals so your existing engineers can maintain it afterward.

A single feature, from scoping to production hardening, usually takes 4–10 weeks depending on how much guardrail and eval work it needs. Ongoing improvement based on production logs can continue as a lighter retainer after launch.

Teams that just need a quick prototype for an internal demo, not a customer-facing feature — for that, a lighter unstructured build is faster and cheaper. This is built for features real users will depend on.

Use-case scoping — defining exactly what the feature needs to do and, just as importantly, where it’s allowed to fail safely. That framing shapes every architecture decision that follows.

It’s a manageable risk, not an unsolvable one. Grounded retrieval, structured outputs, automated evals and human-review checkpoints on anything customer-facing bring the failure rate down to something you can ship with confidence.

Not necessarily — RAG often better serves frequently-changing knowledge, while fine-tuning suits consistent tone or specialized behavior. The right choice depends on the use case.

No — without continuous evaluation, quality regressions from model updates or shifting inputs often go unnoticed until users complain.

Handling the inherent non-determinism of model outputs, along with prompt engineering, retrieval design, and evaluation pipelines rarely needed in traditional software.

Yes — a successful launch can produce an unexpectedly large bill; cost management strategies like caching and prompt optimization are built in from the start.

Yes — grounding responses in retrieved facts, confidence signaling, and human review for high-stakes outputs are core mitigation strategies.

Yes — these LLM-specific security risks are explicitly addressed, since traditional security reviews often miss them entirely.

No — it excels at specific tasks like text generation and summarization, but struggles with precise numerical reasoning or tasks needing perfect factual reliability.

Yes — the architecture and evaluation approach adapts to the specific compliance and audit trail requirements of each regulated industry.

Yes — designed to complement internal teams with specialized LLM expertise for specific features rather than replacing broader engineering capabilities.

No — a rigorous approach involves systematic testing, version control, and understanding how phrasing reliably affects model behavior.

No — added complexity is only justified when it genuinely improves outcomes versus a simpler single-call architecture; each case is evaluated honestly.

Yes — provider updates can change model behavior; monitoring and adaptation strategies are built into ongoing engagements.

No — it's recommended only when the use case genuinely requires consistent specialized behavior not achievable through prompting alone.

Yes — architecture decisions around data handling, retention, and provider selection are made deliberately for sensitive use cases.

Yes — many teams discover the gap to production-grade reliability, cost management, and evaluation is far larger than anticipated.

An initial fit assessment, a rapid prototype phase for feasibility, then production hardening with evaluation pipelines and monitoring.

This scoped engagement suits teams needing specific GenAI expertise for a defined project, rather than continuous broad ML capacity.

Yes — scope can expand with more sophisticated monitoring, cost optimization, or additional features as usage grows.

Yes — all data, prompts, and architecture details are treated as strictly confidential, never referenced publicly without permission.

Yes — continuous evaluation of new releases and their potential to improve existing applications is an ongoing responsibility.

No — the most successful features reliably solve a real user problem day after day, prioritizing reliability over chasing trending capabilities.

Yes — GenAI features are integrated into the client's existing tech stack rather than requiring a full platform migration.

Engagements are scoped to fit specific deliverables, from a focused proof-of-concept to a full production feature build.

Yes — updated regularly as new models and tooling emerge, ensuring recommendations always reflect current best practice.

Yes — features are commonly integrated directly into existing support, CRM, or internal tooling rather than requiring a standalone app.

Yes — ongoing monitoring and support can be arranged after launch to ensure the feature continues performing reliably as usage grows.