✅ Prompt Caching — The Single Biggest Cost Reduction Available in 2025

If there is one API feature that changed how serious Claude applications are built in 2025, it is prompt caching. By marking large, stable portions of your prompt with cache_control: {"type": "ephemeral"}, you instruct Anthropic's infrastructure to retain the computed key-value (KV) state for that section. Subsequent requests that hit the same cached prefix are served at roughly 10% of the normal input token cost and with lower latency. For applications with large system prompts, document corpora, or few-shot example libraries, the savings compound quickly.

Where caching has the most impact

• RAG applications: Cache the document chunks that anchor each session. For a 50,000-token context document, caching reduces the per-query cost by up to 90% from the second query onwards.
• Multi-turn agents: Cache the system prompt + tool definitions + initial instructions. Only the growing conversation history needs to be re-processed on each turn.
• Few-shot classifiers: Cache a large set of labelled examples. New items to classify are appended after the cache boundary — fresh computation applies only to the new item.
• Code assistants: Cache the codebase context (architecture docs, key files). Developer queries are then cheap follow-up prompts rather than full context re-sends.

messages.create(
    model="claude-sonnet-4-6",
    system=[
        {
            "type": "text",
            "text": "You are a code review assistant...",
            "cache_control": {"type": "ephemeral"}  # cache this
        }
    ],
    messages=[{"role": "user", "content": user_query}]
)

✅ Structured Outputs & Tool Use — The Pattern That Unlocked Production Reliability

2025 was the year developers stopped fighting Claude's tendency to produce free-form text and started channelling it. The combination of tool use (forcing Claude to return a structured JSON payload by invoking a defined function) and JSON mode (constraining the output to a schema) has become the de-facto standard for any Claude integration that feeds downstream code. Applications built on this pattern are fundamentally more reliable than those that parse natural-language responses — and they are far easier to test.

The canonical pattern

• Define your output schema as a tool: Even if you don't intend to "call" an external function, defining a tool with a precise JSON schema forces Claude to structure its output to match that schema. Treat the tool as a typed output format.
• Use tool_choice: {"type": "tool", "name": "your_tool"}: This guarantees Claude returns a tool-use block rather than a text response — no parsing ambiguity.
• Validate with Pydantic or Zod: Parse the returned JSON through your schema validator before trusting it. Claude is highly reliable, but validation gives you a safety net and surfaces any regressions immediately.
• Version your schemas: When you change a tool schema, treat it like a database migration — keep the old version live while transitioning, and update your evaluations.