Generative AI & LLM Engineering
Build applications that use large language models with retrieval, tools, structured outputs, evaluation and production controls appropriate to the use case.
Buyer Intent
For LLM Development, prompt/retrieval design is usually the first thing that determines scope, before any question about which model or vendor to use.
For LLM Development, 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 complexity of the retrieval, tool use and structured-output requirements a use case actually needs, not the size of the model chosen. Routing simpler steps to a smaller model while reserving a stronger model for genuinely hard steps is usually both cheaper and more reliable than one model handling everything. Evaluation dataset size, built to reflect real input variety, is the other major driver — and it is what a scoped proof of concept is meant to establish before committing to an architecture.
A company built for LLM development designs structured-output validation, tool orchestration and evaluation around the specific application, rather than treating a single API call as the whole system. Ask a candidate what happens when the model returns output that does not match the expected schema. A firm without a concrete answer for malformed output has not built past the demo stage.
Decision Questions
Look for a partner who designs around the actual use case — retrieval, tool use, structured outputs — rather than defaulting to the largest available model for every task. Ask how they decide which model to use for which step, since routing simpler tasks to a smaller, cheaper model while reserving larger models for harder steps is often both more reliable and more affordable.
Compare evaluation approaches directly. A serious partner tests against a defined set of real examples before and after changes, not just a handful of manual spot-checks. Ask what happens when the model produces structured output that does not match the expected schema, or a tool call that fails — a partner without a concrete fallback answer has not built past the demo stage.
An LLM development engagement should define the retrieval and tool architecture the application needs, set up structured output validation so malformed responses are caught rather than passed downstream, and include an evaluation dataset reflecting real input variety. It should also account for cost and latency at expected usage volume, since these decisions shape which models are viable for the workflow.
The biggest risk is an application that performs well in testing but was never evaluated against the range of real inputs it will actually receive, so failure modes surface only after launch. A second risk is no structured-output validation, which lets a malformed or unexpected model response flow into downstream systems undetected.
Implementation Factors
Practical Delivery
LLM applications are built around the specific task — retrieval, structured extraction, tool orchestration — with model choice following from that task rather than preceding it. Simpler steps route to smaller, cheaper models; harder steps use a stronger model only where the difficulty actually requires it.
Structured outputs are validated against an expected schema so a malformed response is caught before it reaches a downstream system, and the application is evaluated against a dataset that reflects real input variation rather than a small set of favorable examples.
Start with the problem
You do not need a specification. Tell us what you are trying to improve.
