Prompt engineering is the deliberate design and refinement of inputs to a generative model. In a documentation assistant, a prompt combines instructions, retrieved context, and a user question to guide the response. Prompts can also contain structured context such as JSON specifications or API documentation. Because these inputs shape application behavior, treat them as code rather than disposable strings. section 12.1.1 section 13.1
Move a prompt from prototype to production in three steps:
- Make it manageable: version prompts, test changes, and track how different versions perform. Langfuse can manage prompts outside hardcoded application strings and support A/B testing. section 13.1
- Evaluate meaning, not exact wording. An LLM may produce multiple valid answers to the same input, so check factual correctness, scope, and policy alignment. section 13.0
- Trim redundant instructions and formatting, then retest. Fewer tokens reduce cost only if the shorter prompt preserves response quality and safety. section 13.4.4
verbose_prompt = """
You are DakkaBot, DataKrypt's helpful AI assistant designed to help engineers
and developers find information in our comprehensive documentation. Please
carefully read through the provided context below, which contains relevant
information from our knowledge base, and then provide a detailed, accurate,
and helpful response to the user's question. Make sure to be specific and
include relevant details that would help the user accomplish their task.
Context: {context}
Question: {query}
Please provide your response:
"""
The verbose documentation-assistant prompt spells out the role, source, task, and response request, but repeats instructions that consume tokens. section 13.4.4
concise_prompt = """
You are DakkaBot. Answer based on this DataKrypt documentation:
{context}
Q: {query}
A:
"""
The shorter version keeps the documentation context and Q:/A: boundary while using fewer tokens; compare its response quality before adopting it. section 13.4.4
Prompt design also affects surrounding system decisions. A short classification prompt can give a low-cost model enough criteria to route simple, moderate, and complex queries. In a retrieval-augmented system, observing the prompt together with retrieved context and generated output helps locate failures across the chain. section 13.4.2 section 12.1.1
