Beginner’s Guide to Prompt Chaining for Multi-Agentic Systems

Prompt chaining is a powerful approach that allows multiple AI “agents” (often distinct large language models or specialized prompts) to collaborate on complex tasks. Rather than using a single “do-it-all” model, you orchestrate a sequence of smaller, more focused steps—keeping each agent closer to the metal. In this guide, we’ll explore what prompt chaining is, how it works in a Perplexity-like multi-agent system, and practical use cases where you can start applying it right away.

What Is Prompt Chaining?

Prompt chaining is the practice of breaking down a larger AI-driven workflow into smaller, specialized steps, or “links in a chain.” Each link/agent is responsible for a specific subtask, such as:

1. Query Reformulation
2. Data Retrieval
3. Analysis/Summarization
4. Citation/Verification
5. Output Formatting

By dividing the work, you can:

• Debug each stage easily
• Scale or swap agents as needed (e.g., upgrading only your Summarization agent to GPT-4)
• Maintain granular control over the logic behind each step

High-Level Prompt Chain Flow

flowchart LR
    A(User Prompt) --> B[Agent 1: Rewrite / Preprocess]
    B --> C[Agent 2: Data Retrieval]
    C --> D[Agent 3: Analysis / Synthesis]
    D --> E[Agent 4: Final Output]

1. Agent 1 (Rewrite/Preprocess): Possibly cleans up or rephrases user input.
2. Agent 2 (Data Retrieval): Looks up the relevant data or knowledge base.
3. Agent 3 (Analysis/Synthesis): Reads the data, merges insights, answers the question.
4. Agent 4 (Final Output): Formats or polishes the final response.

Understanding Multi-Agentic Systems: The Perplexity Example

Perplexity is known for its multi-agent architecture that excels in search and summarization. Think of it as a pipeline of specialized tasks:

1. Query Agent
   • Takes the raw user prompt and clarifies it for search.
2. Search Agent
   • Queries various sources (web, APIs) to retrieve data.
3. Summarization Agent
   • Analyzes the retrieved documents and compiles a concise response.
4. Citation/Verification Agent
   • Ensures the response is accurate, citing relevant sources.

This multi-step flow can be represented in a more detailed diagram:

flowchart TB
    subgraph Perplexity-Like System
    A1[User Prompt] --> A2[Query Agent<br>(Rewrite or Clarify)]
    A2 --> A3[Search Agent<br>(API/Web Calls)]
    A3 --> A4[Summarization Agent<br>(Analyze & Summarize)]
    A4 --> A5[Citation / Verification Agent]
    A5 --> Output[Final Answer]
    end

Advantages of Multiple Agents

• Transparency: You see exactly how the system transforms your prompt at each step.
• Debuggability: If something fails (like the search step), you can isolate and fix that part.
• Modularity: Swap the Summarization Agent for a more advanced LLM if you need deeper analysis.

Use Cases for Prompt Chaining

1. Complex Knowledge Retrieval

   • Scenario: You have a large library of PDFs or research articles.
   • Chain:
     • Agent 1: Identify relevant keywords from the user question
     • Agent 2: Search or scan documents
     • Agent 3: Summarize findings
     • Agent 4: Format and return the answer

2. Multi-Language Code Editing

   • Scenario: You want to build an AI coding assistant (similar to Aider or Perplexity code).
   • Chain:
     • Agent 1: Parse the user request (e.g., “Refactor this Python function…”)
     • Agent 2: Identify the relevant code segment
     • Agent 3: Propose changes or refactors
     • Agent 4: Test/validate the updated code

3. Data Analysis & Report Generation

   • Scenario: A user wants an analysis of sales data for Q1.
   • Chain:
     • Agent 1: Extract key metrics from a CSV or database
     • Agent 2: Summarize the data trends
     • Agent 3: Generate a final, human-readable report with graphs or bullet points

4. Chatbot With Personality

   • Scenario: You want a chatbot with distinct “voices” or personalities.
   • Chain:
     • Agent 1: Message interpretation / routing (decides which “persona” to use)
     • Agent 2: Persona-based generation (e.g., a “sarcastic” agent or a “helpful” agent)
     • Agent 3: Content moderation (ensure compliance)

Going “Closer to the Metal”

When you run a single large LLM, it’s often a black box. Prompt chaining and multi-agentic systems force you to define:

• System prompts for each agent
• Inter-agent communication format (JSON, text, etc.)
• Conditions for how outputs are passed along

This extra detail:

• Enhances transparency
• Enables targeted fine-tuning (you can optimize a Summarization Agent specifically for summarizing technical documents)
• Improves reliability (one agent’s output can be validated or improved by another agent)

Example of a 3-Step Multi-Agent Chain

Below is an example of a more detailed chain for a user who wants information on “best machine learning practices for medical imaging”:

flowchart LR
    U[User Query: \"Best ML practices for medical imaging?\"] --> Q[Reformulation Agent]
    Q --> S[Search Agent]
    S --> R[Research + Summarization Agent]
    R --> O(Final Answer to User)

1. Reformulation Agent
   • Goal: Clarify or break down the user’s question.
   • Prompt: “Rewrite the question in a formal style for searching medical databases.”
2. Search Agent
   • Goal: Use the refined query to fetch documents or publications.
   • Prompt: “Find relevant academic papers on the topic, focusing on best practices in medical imaging.”
3. Research + Summarization Agent
   • Goal: Sift through the search results, extract key practices, compile a concise summary.
   • Prompt: “Summarize the top 3 best practices from the retrieved articles with references.”

The final user sees a short, direct response with citations, while each agent does its specialized job in the background.

Tips for Building and Scaling Prompt Chains

1. Clearly Define Agent Roles

   • Use a system prompt that explicitly states each agent’s responsibility and output format.

2. Choose the Right Models

   • Not all steps need the same LLM size or capabilities.
   • For example, you can use a smaller model for query rewriting and a larger model for deep summarization.

3. Use State Management

   • Keep track of each agent’s output for debugging or further improvement.
   • Tools like LangChain can handle the transitions between steps.

4. Validate Each Step

   • Insert a “Validation Agent” that checks the output from critical steps (e.g., searching or citing references).

5. Watch Your Costs

   • If you’re calling multiple LLMs in one user query, the tokens add up quickly. Use smaller, cheaper models where possible, and reserve bigger models only for the complex tasks.

Popular Multi-Agent Frameworks to Explore

• LangChain: Offers abstractions like “Chains” and “Agents,” making it straightforward to set up multi-step pipelines.
• HuggingGPT: Uses a larger “planner” model to orchestrate specialized “expert” models.
• Auto-GPT / BabyAGI: Focus on fully autonomous task creation and completion using multiple prompts.
• Perplexity: A production-level example for search & summarization tasks.

Final Thoughts

Prompt chaining and multi-agentic systems are the next evolutionary step for developers who want granular control over AI workflows. By breaking tasks into smaller, specialized prompts, you gain clarity, modularity, and scalability. Whether you’re building a Perplexity-like search engine, a code refactoring assistant, or a data analysis tool, the principles remain the same: define each agent’s role, handle intermediate outputs carefully, and chain everything together in a logical sequence.

Key Takeaways

• Subdivide tasks into smaller, manageable steps or “agents.”
• Leverage smaller LLMs for simpler tasks, saving bigger models for complex ones.
• Adopt existing frameworks (LangChain, HuggingGPT, etc.) or build your own custom orchestrations.
• Iterate and refine continuously by monitoring each agent’s performance and output.

By fully understanding and embracing prompt chaining, you’ll not only improve your AI system’s performance but also create workflows that are easier to debug, upgrade, and trust. Happy chaining!