Prompt Chain Builder

Prompt chaining is a technique where the output of one LLM call is used as input to the next. Instead of trying to do everything in a single prompt, you break complex tasks into sequential steps — each step focused on a specific sub-task. This mirrors how humans solve complex problems: research first, then analyze, then synthesize. Chains are easier to debug, test, and optimize than monolithic prompts because each step has clear inputs and expected outputs.

Use a single prompt for simple, well-scoped tasks: classification, summarization of short text, translation, code completion. Use prompt chaining (chain-of-thought) when: the task requires multiple distinct reasoning steps, when you need intermediate results for validation, when different steps may benefit from different models or system instructions, or when the output of one step needs to be used by a parallel branch. Rule of thumb: if you can describe the task in one sentence with one expected output format, a single prompt is fine. If you need a paragraph with multiple stages, chain it.

In most production implementations, you maintain an in-memory context object. After each LLM call completes, you store the output under a key (e.g., step1_output) and inject it into the next prompt by string interpolation. In this builder, the {{step1_output}} placeholder syntax shows where to inject the value. In code: const step2Prompt = template.replace('{{step1_output}}', step1Result). Frameworks like LangChain, LlamaIndex, and custom orchestrators all handle this pattern, but the underlying mechanism is simple string substitution.

Long, complex prompts tend to degrade quality — models struggle to track all requirements simultaneously. Chains improve quality by letting each step focus on one thing. They also enable: (1) validation gates between steps — check if step 1 output is correct before proceeding; (2) branching — send different steps to different models optimized for that task (e.g., GPT-4o for reasoning, Claude Haiku for summarization); (3) parallelism — run independent steps concurrently; (4) observability — log and debug each step independently. The main tradeoff is increased latency and API cost proportional to the number of steps.