Prompt Engineering · LLM Optimization
Prompt engineering services that make Claude and OpenAI models reliable in production.
Prompt engineering is the discipline of designing, structuring and testing the instructions given to a large language model — system prompts, examples, tool definitions and context — so it produces reliable, correct output for a specific task instead of relying on chance phrasing. Optraject engineers and evaluates prompts like software, for Claude, the OpenAI API, Azure OpenAI and AWS Bedrock.
What does prompt engineering with Optraject include?
01
System-prompt design
Clear, testable system prompts that define role, constraints, tone and output format — built to survive edge cases, not just the demo.
02
Prompt evaluation & testing
Evaluation harnesses with representative test cases, tracking accuracy, consistency, latency and cost across model and prompt versions.
03
Retrieval-augmented generation (RAG)
Grounding model output in your own documents and data via vector databases, reducing hallucination and keeping answers current.
04
Tool & function design for agents
Tool schemas and system prompts engineered for agentic use with LangGraph and the Model Context Protocol (MCP) — where reliability matters more than a clever one-off answer.
05
Fine-tuning assessment
An honest comparison of prompting versus fine-tuning for your specific task, volume and cost constraints — before you commit engineering budget either way.
06
Team training
Hands-on enablement so your own product and engineering teams can maintain and extend prompts and evaluations after we hand over.
When does prompting beat fine-tuning?
Prompting — including few-shot examples and retrieval-augmented generation — usually wins when requirements change often, data is limited, or you need to ship in days rather than months. Fine-tuning can make sense for narrow, stable tasks at high volume where prompting alone can't hit the required accuracy or cost per call.
Choose prompting + RAG when
Requirements shift frequently, you need answers grounded in current documents, or you want to ship and iterate within weeks rather than months.
Consider fine-tuning when
The task is narrow and stable, volume is high, and prompting alone can't reach the accuracy, latency or per-call cost your business case requires.
How we engineer and test a prompt
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Step 1
Define
We define the task, success criteria and edge cases up front — the same discipline as writing a specification for regular software.
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Step 2
Engineer & evaluate
We draft the prompt, build an evaluation set, and iterate against measured accuracy and consistency rather than gut feel.
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Step 3
Ship & monitor
We deploy with monitoring for drift and regressions, so future model or prompt changes don't silently break production behavior.
Frequently asked questions
What is prompt engineering?
Prompt engineering is the discipline of designing, structuring and testing the instructions given to a large language model — system prompts, examples, tool definitions and context — so it produces reliable, correct output for a specific task, rather than relying on chance phrasing.
When does prompting beat fine-tuning?
Prompting (including few-shot examples and retrieval-augmented generation) usually wins when requirements change often, data is limited, or you need to ship in days rather than months. Fine-tuning can make sense for narrow, stable tasks at high volume where prompting alone can't hit the required accuracy or cost per call. Optraject evaluates both and recommends the cheaper, more maintainable option first — usually prompting plus RAG.
How do you test and evaluate prompts?
We build evaluation harnesses with representative test cases and success criteria, run them against every prompt change, and track accuracy, consistency, latency and cost over time — the same rigor as automated testing in traditional software engineering, so prompt changes don't silently regress production behavior.
Can you design system prompts and tool definitions for AI agents?
Yes. Agent reliability depends heavily on how tools are described and how the system prompt constrains behavior. We design and test tool schemas and system prompts specifically for agentic use with frameworks like LangGraph and the Model Context Protocol (MCP), not just single-turn chat.
Do you train our team in prompt engineering?
Yes, we offer hands-on training so your product and engineering teams can maintain and extend prompts themselves after the engagement, including our evaluation practices and guidance on Claude and OpenAI model-specific behavior.
Which models do you optimize prompts for?
Primarily Claude and OpenAI API models, including deployments via Azure OpenAI and AWS Bedrock. Prompting techniques differ meaningfully between model families, so we tune and evaluate per model rather than assuming one prompt works everywhere.
Want prompts that hold up in production?
Tell us what you're building — we'll assess whether prompt engineering, RAG, or a different approach is the right fit.
Contact us