Generative AI & LLM Engineering
Plan and implement generative AI around specific business workflows, data boundaries and measurable acceptance criteria rather than generic model access.
Buyer Intent
For Generative AI Consulting, prompt/retrieval design is usually the first thing that determines scope, before any question about which model or vendor to use.
For Generative AI Consulting, the useful starting point is a concrete workflow, decision or system boundary. PlanckCyber defines the outcome, current baseline, dependencies and controls before deciding whether AI, standard software or a combination is the right implementation.
Common Questions
Cost follows the specificity of the workflow being supported, not which model gets used. A narrowly scoped task with a clear definition of a correct answer is cheaper to build and evaluate than an open-ended assistant expected to handle many kinds of requests well. Evaluation dataset size and how much prompt or retrieval iteration a use case needs are the real cost drivers, which is why scoping the workflow first matters more than comparing model prices.
A company built for this work starts by defining the specific workflow and what a correct answer looks like, then designs prompts, retrieval and evaluation around that definition. Ask a candidate to describe their evaluation dataset approach for a past project. A firm that cannot explain how they measured quality beyond a handful of manual examples is describing a demo, not a production system.
Decision Questions
Look for a partner who asks what specific workflow the generative AI is supposed to improve before discussing which model to use. A partner who leads with model capabilities rather than the business problem is more likely to produce an impressive demo than a system that holds up under real, varied input from actual users.
Compare how each firm handles prompt and retrieval design versus how much they rely on a model's general capability alone. Ask what their evaluation plan looks like — a defined set of test cases checked before and after changes — and what happens when the model produces a wrong or unsafe output. Vague answers about "the model is pretty good" are a warning sign.
A generative AI engagement should define the specific workflow and acceptance criteria before prompt or retrieval work starts, include an evaluation dataset that reflects real input variation, and account for model cost and latency at the expected usage volume. It should also define fallback behavior — what the system does when unsure, rather than always producing a confident-sounding answer.
The biggest risk is skipping evaluation and shipping based on how the system performed on a handful of hand-picked examples, which hides how it behaves on the messier inputs real users actually provide. A second risk is underestimating cost and latency at real usage volume, which can make a technically working system too slow or expensive to run as designed.
Implementation Factors
Practical Delivery
Generative AI work is scoped to a specific workflow first — what decision or task the output supports — before any prompt or model selection happens. That scope defines what a correct answer looks like, which is what evaluation is measured against later.
Cost and latency are estimated at expected usage volume, not just checked in a low-traffic test, and the system includes defined fallback behavior for low-confidence cases instead of always producing a confident-sounding response regardless of certainty.
Start with the problem
You do not need a specification. Tell us what you are trying to improve.
