What Is Prompt Chaining? The Technique That Makes Complex Agents Reliable

When you give a single AI prompt a complex task, you are asking it to do several fundamentally different types of thinking at once. Prompt chaining fixes this — and it is the single biggest quality improvement available to intermediate agent builders.

If you have been building AI agent workflows for a while, you have probably noticed something: the more complex the task you give a single prompt, the less reliable the output. A prompt that asks the AI to "research this company, find their pain points, and write a personalised cold email" produces output that is mediocre at all three things.

Prompt chaining is the solution. It is also the technique that separates beginner automations from professional ones.

The Core Idea

Prompt chaining breaks a complex task into a sequence of simpler, focused prompts — where the output of each prompt becomes the input to the next. Instead of one prompt trying to research, analyse and write simultaneously, you have three prompts that each do one thing extremely well.

The difference in output quality is significant. A chain consistently produces better results than a single mega-prompt because each model call is performing one cognitive task with full attention. The research prompt does only research. The analysis prompt does only analysis. The writing prompt does only writing — with the benefit of the two previous outputs as context.

A Concrete Example: The Lead Email Chain

Here is the same task handled two ways.

Single prompt approach:

Research Acme Corp and write a personalised cold email to their VP of Sales about our sales automation product.

This prompt asks the AI to research the company, understand the person's role, identify relevant pain points, and write a compelling email — all at once, in one model call. The result is usually generic: a research summary that reads like a Wikipedia article and an email that could have been written for any company.

3-step chain approach:

Prompt 1: Research Acme Corp. Return ONLY this JSON: company overview (2 sentences), main product (1 sentence), employee count, recent news (3 bullets, last 6 months), funding stage.

Prompt 2: From this company profile: [Prompt 1 output]. Identify their top 3 pain points that a B2B sales automation tool would solve. Return as a numbered list. One sentence each. Be specific to their situation.

Prompt 3: Write a personalised 80-word cold email to a VP of Sales at Acme Corp. Use this context: [Prompt 1 output] and these pain points: [Prompt 2 output]. Reference one specific company detail. End with a soft CTA.

The chain produces a research brief that is structured and accurate, pain points that are genuinely specific to the company's situation, and an email that actually references real, specific details. The difference is not subtle — it is the difference between output you need to rewrite and output you can send.

The Four Chain Patterns

Sequential chain is the most common. Each prompt receives the previous output and builds on it. Research → Analysis → Write → Format. Linear, auditable, easy to debug. Best for content production, research synthesis and lead outreach workflows.

Branching chain adds a decision point. After one prompt classifies or evaluates the input, different subsequent prompts handle each case. An email arrives → AI classifies as Urgent, Reply or FYI → each branch runs different prompts and actions. Make.com's Router module implements this visually without any code.

Parallel chain runs multiple prompts simultaneously on the same input, then combines the outputs. Three different analyst prompts review a document simultaneously, then a fourth prompt synthesises their findings. Faster than sequential for independent subtasks. In Make.com, use multiple simultaneous API calls followed by an aggregation step.

Iterative chain runs a prompt, evaluates the output quality with another prompt, and loops back with feedback if the quality is insufficient. More API calls, higher cost — but produces significantly higher quality output for creative or high-stakes content. Best reserved for content that represents your brand publicly.

Building Your First Chain in Make.com

The mechanics are simpler than they sound. In Make.com, a prompt chain is three OpenAI modules connected in sequence. The output of module 1 is mapped to the input of module 2. The output of module 2 is mapped to the input of module 3.

The critical implementation detail: instruct each prompt to return structured output — ideally JSON — so the next module can reliably extract the specific fields it needs. A prompt that returns unstructured prose is harder to work with in subsequent steps. A prompt that returns clean JSON makes the next step trivial.

Set temperature to 0 on research and classification prompts for consistent, predictable output. Use higher temperature (0.5-0.7) on creative writing prompts where you want variation and originality.

The Three Most Common Chaining Failures

Output too long for the next prompt. If prompt 1 returns 3,000 tokens of research, it can crowd out the instructions in prompt 2. Fix: add "Return your output in under 200 words" or add a compression step between long research prompts and subsequent writing prompts.

Structured data lost between steps. Prompt 1 returns a formatted list. Prompt 2 receives it as a wall of text. Fix: instruct prompt 1 to return JSON. Use Make.com's JSON parser module to extract specific fields before passing to prompt 2.

Error in one step crashes the chain. Prompt 2 fails or returns empty output. Prompt 3 receives nothing and produces garbage. Fix: add an error handler after each LLM module. If the output is empty or contains "I cannot", route to an error notification instead of continuing the chain.