Prompt Chaining

Prompt chaining is the architectural pattern where a complex task is decomposed into a sequence of simpler LLM calls, with the output of each call feeding the next, rather than asking one model to do everything in a single prompt. The motivation is that LLMs perform better on narrow, well-defined subtasks than on sprawling end-to-end tasks; chaining lets you specialize prompts per step, insert validation or human review between steps, and use cheaper models for easier subtasks. A canonical example: a contract-review chain might (1) extract clauses with a small fast model, (2) classify each clause by type with a classifier, (3) flag risky clauses with a reasoning model, (4) draft suggested edits with a domain-tuned model, (5) summarize the review for the human reviewer. Each step has its own prompt, model choice, and evaluation. Implementation frameworks include LangChain (the original chain abstraction), LangGraph (graph-structured agent workflows), DSPy (compile-time-optimized chains), LlamaIndex (data-centric chains), and Anthropic's Claude with native tool-use orchestration. The trade-offs: chained workflows are more predictable and easier to debug than single mega-prompts, but they accumulate latency (each step adds round-trip time) and cost (each step is its own API call). Chaining interacts with structured outputs — every intermediate step should produce a typed schema that the next step consumes, with validation at each boundary. AI governance teams treat prompt chains as deployable artifacts with full version control, integration testing, and per-step audit logging — the chain is effectively a small application, not just a prompt.

Chains as governed workflows: Centralpoint manages prompt chains as governed workflow artifacts with audit logging at every step — the same workflow discipline Oxcyon has applied to enterprise content publishing for 25 years. Chains run on-premise, tokens meter per skill and per step, and chained chatbots deploy through one line of JavaScript.


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