Hire Generative AI Developers

The model call is the smallest part of a generative AI feature. Most of the engineering sits around it. For a retrieval-augmented generation (RAG) assistant that means an ingestion pipeline that parses PDFs, wikis and tickets, splits them into sensibly sized chunks, stores embeddings alongside the original text and permissions, and keeps that index fresh as documents change. Retrieval is usually hybrid - keyword plus vector search - often followed by a reranking step, and answers carry citations back to the source so users can check them.

Agents add another layer: the model chooses tools such as a search, a database query or an API call, and the developer decides which tools exist, what each one is allowed to touch, and where a human has to approve the action. A good GenAI developer keeps agents narrow and observable - every step logged, every tool call bounded - because an agent with broad permissions and no trace is very hard to debug or trust.

Evaluation is what separates a demo from a feature. A capable developer builds a test set of real questions with expected answers or grading criteria, runs it on every prompt, model or retrieval change, and tracks whether quality went up or down. Automated grading with a second model is useful at scale, but it should be spot-checked by people who know the domain.

Guardrails sit on both sides of the model: input checks for abuse and prompt injection - including instructions hidden inside retrieved documents - and output checks such as schema validation, PII redaction and refusing to answer when retrieval finds nothing relevant. Cost and latency are design decisions too. Caching repeated prompts, routing simple requests to smaller models, trimming context, setting per-user token budgets and streaming responses all change the bill and the user experience far more than prompt wording does.

Ask candidates to walk you through a GenAI system they shipped: how they measured quality, what broke after launch, and what they changed as a result. Strong engineers talk about failure cases, evaluation data and trade-offs between models; weaker ones talk mostly about prompts. A practical exercise works well - hand over a small set of messy real documents and ask for a retrieval approach plus a way to prove it works.

Watch for red flags: no answer to how they would detect a regression, no plan for data privacy or which data may be sent to a third-party model, and a habit of reaching for fine-tuning before trying better retrieval and prompting. Also check ordinary software skills - APIs, queues, testing and observability - because a GenAI feature is still a production service.

Agree on what good looks like before building: the tasks the feature must handle, the ones it should refuse, and how accuracy will be judged. Share a representative sample of real user questions and real documents early - synthetic examples hide the problems that matter. Expect iteration; first versions typically improve most through better data and retrieval rather than cleverer prompts.

Plan for change after launch. Model providers update and retire models, so keep the evaluation suite as a permanent asset and rerun it before switching versions. Keep prompts, configuration and evaluation data in your own repository so the work stays yours - at PixoBots the client owns the code and IP.