AI/ML
Prompt Engineering
What prompt engineering is and why it matters
Definition
Prompt engineering is the discipline of designing, testing, and iterating on the text instructions (prompts) given to a language model to produce accurate, consistent, and useful outputs. It includes techniques like few-shot examples, chain-of-thought reasoning, system prompts, and structured output formatting to control model behavior for production applications.
How it works
The difference between a useful AI feature and a frustrating one often comes down to the prompt. A vague instruction like "summarize this document" produces inconsistent results. A well-engineered prompt specifies the format, length, audience, and what to include or exclude. Good prompt engineering makes the same model dramatically more useful.
Key techniques include few-shot prompting (providing examples of desired inputs and outputs), chain-of-thought (asking the model to reason step by step), system prompts (setting persistent behavioral rules), and structured output (requesting JSON, markdown, or other parseable formats). Each technique trades off between token usage, latency, and output quality.
In production systems, prompts are treated as code. They are version-controlled, A/B tested, and evaluated against test datasets. A prompt that works 90% of the time in testing might fail on edge cases in production. We maintain prompt evaluation suites that automatically check for regressions when prompts change.
How 1Raft uses Prompt Engineering
Prompt engineering is our first tool for every AI feature. Before fine-tuning or building complex pipelines, we optimize the prompt. In a legal tech project, structured prompts with chain-of-thought reasoning improved contract clause extraction accuracy from 72% to 94% - without changing the model. We treat prompts as versioned artifacts and evaluate them systematically against labeled test data.
Related terms
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Fine-Tuning
Fine-tuning is the process of training a pre-trained AI model on a smaller, domain-specific dataset to adapt its behavior for a particular task. It modifies the model's internal weights so it performs better on your specific use case without training from scratch.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Token (AI Context)
A token is the basic unit of text that a language model processes. Words, parts of words, and punctuation are all broken into tokens. Token counts determine model costs, context window limits, and response length constraints.
AI/ML
AI Hallucination
AI hallucination is when a language model generates information that sounds plausible but is factually incorrect or entirely fabricated. It is a fundamental behavior of LLMs, not a bug, and managing it is essential for any production AI application.
AI/ML
Model Inference
Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. When you send a prompt to an LLM and get a response, that is inference. It is where compute costs, latency, and user experience are determined.
Related services
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