Anthropic

Good Claude prompts are not mysterious incantations. They are interfaces. A strong prompt tells the model what task it is performing, which information matters, what constraints apply, and what successful output looks like. The more consequential the application, the more that interface should be tested like code rather than tuned by intuition. Claude’s current prompting guidance starts with an important prerequisite: define success criteria and build a way to evaluate them before spending time on prompt tricks. That aligns with the broader AI evaluation fundamentals principle that improvement needs repeatable…

A large context window gives Claude more working space, but it does not make every token equally useful. The context window includes the system prompt, conversation history, documents, images, tool definitions, tool results, and the model’s output. As that working set grows, the engineering problem shifts from “can it fit?” to “what deserves to stay?” This matters across the Claude certification ecosystem because long-running agents, coding workflows, document analysis, and tool-heavy applications all depend on context discipline. The largest nominal window can still produce weak results if it is filled…

Model Context Protocol gives Claude a standardized way to discover and use external tools and data sources. In a production design, that means you can connect Claude to an MCP server rather than inventing a different integration format for every capability. The protocol does not remove the need for security, tool design, or observability; it gives those concerns a common interface. For teams building on Claude, MCP is especially useful when several tools belong to one governed service or when the same capabilities should be available across multiple agent workflows….

Retrieval-augmented generation is useful when a Claude application must answer from private, changing, or tightly governed information rather than only from the model’s learned knowledge. The pattern sounds simple—retrieve relevant material, add it to context, and ask Claude to answer—but production quality depends on how sources are prepared, filtered, ranked, cited, evaluated, and refreshed. The general mechanics in embeddings, vector databases, and RAG provide a useful foundation. A Claude-specific design adds several practical considerations: Anthropic does not currently provide its own embedding model, search-result blocks can carry source attribution into…

A Claude agent becomes useful when it can move through several steps, call tools, observe results, and continue toward a goal. Reliability, however, does not come from autonomy alone. It comes from the loop around the model: explicit state, narrow tool contracts, stop conditions, typed errors, retries, permissions, and evidence that the task actually finished. The general concepts in AI agents fundamentals explain goals, memory, planning, tools, and feedback. Claude adds a concrete execution contract built around structured tool requests and results. Understanding that contract makes agent behavior easier to…

Claude applications change even when the product requirement stays the same. Prompts evolve, models are upgraded, retrieval is retuned, tools are added, and real users expose edge cases that the original design never anticipated. Evaluation turns those changes into measurable engineering decisions instead of subjective impressions. The general method in AI evaluation fundamentals is a strong starting point. Production Claude systems also need to measure long-context behavior, tool trajectories, guardrail performance, model migrations, latency, cost, and failures that appear only after several steps. Define success before building the test set…

A Claude application needs guardrails because the model operates inside a larger system of users, documents, tools, identities, and side effects. Safety is not one instruction at the top of a prompt. It is the combination of input handling, permission boundaries, tool validation, approvals, observability, and recovery behavior. The same separation is visible in tool use and function calling: Claude can propose an action while the surrounding application controls execution. Guardrails are strongest when each rule is enforced by the layer that actually owns the risk. Separate trusted instructions from…

Enterprise Claude deployments create a data-governance problem that is broader than model quality: what information may enter the system, which product surface processes it, where copies are retained, who can access them, what external services receive data, and how deletion or audit requirements are handled. Those questions should be designed into the architecture. The broader principles in data governance, catalogs, and lineage remain relevant. Claude adds model-specific surfaces such as API messages, enterprise conversations, file uploads, tools, workspaces, and third-party integrations that need explicit ownership and lifecycle rules. Classify the…