LLM Development
The unglamorous, essential part of LLM features: evaluation, prompt design, and picking the right model for the job.
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LLM development is the work of building reliable features on top of large language models — prompt design, output evaluation, choosing between models and providers, and deciding when retrieval, fine-tuning, or a simpler approach is the right tool. DuCodes treats this as an engineering discipline with its own testing and iteration cycle, not a one-time prompt written and forgotten.
Behind every working LLM feature is a body of unglamorous work: prompts that get iterated against real inputs, an evaluation process that catches regressions when a prompt or model changes, and a deliberate choice about which model actually fits the task's accuracy, latency, and cost requirements. We treat that as core engineering, not an afterthought around an API call.
This includes selecting and comparing models (OpenAI, Anthropic, Gemini, and open-source options where self-hosting makes sense), designing prompts and output schemas, and building the evaluation harness that tells you when a change actually improved things versus just felt like it did.
Why work with DuCodes on this
Evaluation, not vibes
We test prompt and model changes against a real set of inputs with expected outputs, rather than judging quality by a handful of manual spot checks.
Model choice based on your requirements
We'll recommend the model (or combination of models) that fits your actual accuracy, latency, and cost needs — not whichever is most talked about.
Prompts treated as versioned, tested code
Prompt changes go through the same review and testing discipline as application code, since a small wording change can meaningfully shift output quality.
Knows when NOT to use an LLM
Some parts of a workflow are more reliably handled by deterministic code — we'll say so rather than routing everything through a model by default.
How we work
Define the task and success criteria
What does a correct output actually look like, and what error rate is acceptable?
Model & approach selection
Comparing models and techniques (prompting, RAG, fine-tuning) against your actual requirements.
Build the evaluation harness
A real test set so future changes can be measured, not just guessed at.
Iterate on prompts and pipeline
Structured iteration against the evaluation set, not ad hoc tweaking.
Deploy & monitor drift
Ongoing tracking, since model updates and changing usage patterns can shift output quality over time.
Technology we use
Frequently asked questions
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