// GLOSSARY -- AGENTIC AI

What is Prompt Chaining?

1 min read Updated

Prompt chaining is the technique of connecting multiple LLM calls in sequence, where each call's output feeds into the next call's input — enabling complex multi-step reasoning and task completion.

WHY IT MATTERS

Prompt chaining breaks complex tasks into manageable steps. Instead of asking one massive prompt to do everything, you create a pipeline: Step 1 analyzes the input, Step 2 plans the approach, Step 3 generates the output, Step 4 validates quality.

Each step can use different prompts, different models, and different temperatures optimized for its specific task. Analysis steps might use a reasoning model; generation steps might use a creative model.

For financial agents, prompt chaining creates structured decision-making: analyze market data → evaluate options → generate trade plan → validate against policies → execute. Each step is independently optimizable and auditable.

FREQUENTLY ASKED QUESTIONS

Prompt chaining vs single prompt?
Single prompts are simpler but less reliable for complex tasks. Chains give more control, better error handling, and easier debugging — you can see where things went wrong.
How many steps is typical?
2-5 steps for most tasks. More steps add latency and cost. Each step should do something meaningful — don't chain for the sake of chaining.
Can chains be parallelized?
Independent steps can run in parallel (research + data retrieval). Dependent steps must be sequential (generate → validate). Good chain design identifies parallelizable steps.

FURTHER READING

Enforce policies on every tool call

Intercept is the open-source MCP proxy that enforces YAML policies on AI agent tool calls. No code changes needed.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.