LLM Engineering / Prompt Engineering
Prompt Engineering
The same model can give excellent or useless results depending on the prompt, examples, and output format. We design, test, and version prompts for your real use cases — measured against evaluation data — so outputs are consistent, structured, and ready for production.
This Is For You If...
Your LLM feature gives inconsistent or off-target results
Outputs need a strict format, like JSON, and sometimes break it
You're switching models and prompts behave differently
Prompts are edited directly in production with no testing
Which Area Matches Your Need?
Ask Yourself | Points Toward |
Are outputs inconsistent in quality? | Prompt Design & Testing |
Do outputs need a fixed structure? | Structured Outputs |
Are you changing models? | Model Migration |
Are prompt changes risky? | Prompt Versioning & Management |
Prompt Engineering Services We Offer
Prompt Design & Testing
System prompts, instructions, and examples tested against a set of real inputs.
Structured Outputs
JSON schemas, function calling, and validation so outputs can be used directly by your code.
Model Migration
Adapting prompts when moving between providers or model versions, with side-by-side comparison.
Prompt Versioning & Management
Storing, versioning, and testing prompts like code before changes reach users.
What We Need From You
What to Share | Why It Helps |
Current prompts and model | The starting point |
Example inputs and ideal outputs | Used to test and measure |
Where outputs are used | Defines format and quality needs |
Known failure examples | Show what to fix first |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Tweaking prompts on one example | Fixes one case, breaks others | Test against a full set of inputs |
Parsing free text from the model | Breaks on small variations | Use structured outputs |
Very long, rule-heavy prompts | Instructions conflict and get ignored | Keep prompts focused and test each rule |
Changing prompts in production | Users find the regressions | Version and test before release |
How It Works
1. Collect examples
Real inputs and expected outputs
2. Baseline
Measure current results
3. Iterate
Prompts and formats improved and re-measured
4. Integrate
Structured outputs wired into your code
5. Hand over
Versioned prompts and tests
Why Codersarts
We treat prompts like code — tested, versioned, and measured — so improvements are real and don't break other cases.
Turnaround Time
A focused prompt improvement for one feature is typically delivered within a few days. Larger prompt systems are scoped individually.
FAQ
Is prompt engineering enough?
Often, yes; sometimes retrieval or fine-tuning is needed. See RAG Systems.
Which models do you work with?
OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, and others.
How do you measure improvement?
With an evaluation set — see LLM Evaluation.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Are Your LLM Outputs Unpredictable?
Get prompts that are tested, structured, and consistent.