Prompt Engineering & Model Interaction

Context engineering is the design of the information a language model receives at each step of an application. It covers instructions, conversation history, retrieved evidence, tool definitions and state, with the aim of giving the model enough relevant context to act reliably within a finite context window.

Synthetic data generation creates examples using a model or a programmed process rather than collecting each example directly from the target setting. In language-model applications, it can produce demonstrations, test cases or training records, but usefulness depends on validation and coverage rather than the volume generated.

LLM decoding strategies determine how an application selects the next token from a language model's predicted distribution. Greedy selection, sampling and beam search make different trade-offs between repeatability, diversity and search effort; temperature and probability filters modify selection rather than adding knowledge to the model.

Prompt caching reuses computation for a repeated input prefix so that later model requests need less repeated processing. It is an inference optimization for shared instructions or context; it differs from returning a previously generated answer because the model can still produce a new response to each request.

Token optimization reduces unnecessary input or output tokens while preserving the information and behavior an application needs. It includes selecting context, shortening repetitive instructions, controlling generated length and choosing suitable representations; the target is useful task completion per resource spent, rather than the shortest possible prompt.

The Anthropic API is a developer interface for sending inputs to Claude models and receiving generated responses. The skill involves translating application requirements into supported message formats, tool interactions and streaming behavior, then handling authentication, usage, errors and model-specific capabilities as part of a reliable service integration.

The OpenAI API exposes models and tools through interfaces that developers can integrate into their own applications. Competence involves constructing supported requests, managing response and tool lifecycles, and building reliable error, usage and evaluation handling around the selected API rather than treating a generated answer as a complete application.

LLM API integration connects a model service to application data, workflows and user interfaces. It requires more than sending a prompt: the integration must preserve task state, validate outputs, control tool execution and handle timeouts, limits and changing provider interfaces in a way the rest of the application can depend on.

Semantic routing chooses a processing path from the meaning of an incoming request. A router may compare query embeddings with example routes or use a classifier to select a tool, workflow or model; the key skill is making routing decisions measurable and handling uncertain or overlapping cases explicitly.

In-context learning is a model's use of examples or task information supplied in its input to guide a new response without updating its weights. A few labeled examples can communicate a mapping, format or convention, although the result remains sensitive to which demonstrations are chosen and how they are presented.

Prompt engineering is the design and evaluation of instructions, examples and output requirements used to guide a language model on a task. It turns a vague request into an explicit interaction contract, then tests whether that contract produces useful behavior across representative inputs and failure cases.

System prompt design establishes persistent instructions for an assistant's role, behavior and interaction with tools. It defines how the model should approach a task and handle uncertainty or conflicting input, while recognizing that application permissions and critical checks must be enforced outside natural-language instructions.

Prompt management treats prompts as versioned application artifacts with review, evaluation and controlled release. It connects the text used in model calls to its owner, parameters and observed behavior, so teams can compare changes, reproduce incidents and roll back a prompt that worsens results.

Automated prompt optimization searches for instructions or demonstrations that improve a measured task objective. Candidate prompts may be generated or revised by a model, selected through trial performance or evolved with search; the optimized artifact remains a prompt rather than a newly trained base language model.

DSPy is a framework for building language-model programs as composable modules with declared input and output roles, then optimizing their instructions or examples against a metric. It makes prompt behavior part of a program and evaluation workflow, while leaving the developer responsible for the task specification and evidence of improvement.

Program-Aided Language Models use a language model to translate a problem into executable code and use a runtime to perform the resulting calculation. PAL separates interpreting the request from carrying out exact operations, so a useful answer depends on both the generated program and controlled execution of that program.

Self-consistency samples several reasoning paths and aggregates their final answers instead of trusting one generation. The method seeks a result supported by multiple sampled paths, commonly through majority voting, while recognizing that agreement among outputs from the same model is different from independent verification of the answer.

Structured LLM outputs constrain or validate model responses against a machine-readable shape such as a JSON schema. They make generated content easier for application code to consume, but a response satisfying the schema can still contain incorrect facts, unsupported inferences or values that violate the application's business rules.

Test-time compute scaling spends additional inference resources on solving a request instead of only enlarging or retraining the model. Extra reasoning, multiple candidates, search and verification are different ways to use that budget; the useful question is whether they improve task outcomes enough to justify their cost and latency.

The Google Gemini API provides developer access to Gemini models for supported text and multimodal tasks. The integration skill covers request construction, content and tool handling, streaming and operational controls, with explicit attention to the differences between model capabilities, API surfaces and the requirements of the application using them.

Chain-of-thought prompting asks a language model to produce intermediate reasoning steps before or alongside an answer. It can help some multi-step tasks by providing a pattern for decomposing the problem, but the visible explanation is generated text and should not be treated as a faithful record of the model's internal computation.