Webinar · Jul 20

Fine-tuning open-source models: is it time to move off Frontier Lab models?

Build AI agents and LLM workflows

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Guide

Prompt chaining, explained

What is prompt chaining?

Prompt chaining is the practice of breaking a task into a sequence of LLM calls, where each step's output becomes the next step's input. Instead of asking one giant prompt to extract data, reason over it, and format a response all at once, you split the work into focused subtasks and pass state between them.

The classic example is document question-answering. One prompt extracts the relevant quotes from a long document; a second prompt takes those quotes and the original question and writes the final answer. Each prompt does one job well, and the intermediate output is inspectable.

Chaining helps whenever a single prompt is doing too much. It improves reliability (each step is easier to get right), transparency (you can log and debug intermediate results), and controllability (you can add validation, retries, or branching between steps). It is the foundation of almost every real LLM application, from RAG pipelines to multi-step data extraction.

Prompt chaining vs. agents vs. orchestration

These terms overlap, but they describe different levels of autonomy.

Prompt chains are fixed pipelines. You decide the steps ahead of time — step one runs, then step two, then step three — and the control flow is deterministic. Chains are predictable, easy to test, and easy to debug, which is why most production systems are chains with a little branching, not fully autonomous agents.

Agents add a decision loop. Instead of a hard-coded sequence, the model chooses which tool to call next based on the current state, loops until a goal is met, and can recover from errors on its own. Agents are more flexible but harder to make reliable, because the path through your system is decided at runtime.

LLM orchestration is the layer that runs either pattern in production: managing state between steps, calling tools and external APIs, handling retries and parallelism, and coordinating multiple prompts or models. Prompt orchestration specifically refers to sequencing and versioning the prompts inside that flow. Frameworks like LangGraph or a visual agent builder give you the orchestration primitives; whether you build a fixed chain or an autonomous agent on top is your design choice.

How to build an LLM workflow

Building a reliable LLM workflow is less about clever prompting and more about disciplined engineering. A repeatable process looks like this:

1. Decompose the task. Write out the end-to-end job and split it into discrete steps that each have a single responsibility — extract, classify, retrieve, generate, validate. If a step is hard to describe in one sentence, split it further.
2. Define nodes. Turn each step into a node with a clear input and output contract. A node is usually a versioned prompt, but it can also be a tool call, a retrieval step, or a bit of code that transforms state.
3. Pass state. Decide what data flows between nodes and in what shape. Keeping a structured state object (rather than shoving everything into free text) is what makes chains debuggable.
4. Add tools. Give steps access to retrieval, function calls, or external APIs where the model needs real data or actions instead of guessing.
5. Test end to end. Run the whole workflow against real inputs and inspect every intermediate output. Tool mocking lets you test agent and prompt behavior without standing up live integrations, so you can validate logic before wiring in production APIs.
6. Version everything. Version the prompts, the workflow structure, and the tools together, so you can roll back a bad change and compare versions with evaluations instead of guessing.

AI agent builders & workflow tools compared

The market for AI agent builders splits into two camps. Code-first frameworks — LangChain/LangGraph, CrewAI — give engineers maximum control through Python or JavaScript, at the cost of a steeper learning curve and more scaffolding. Visual builders — n8n, Flowise, Dify — offer a drag-and-drop canvas that gets a working pipeline running fast, and are often the better fit when AI is one step inside a larger automation. The comparison below focuses on how each tool handles llm orchestration versus the prompt-management lifecycle around it.

When to chain prompts vs. use a single prompt

Chaining is powerful, but every extra step has a cost. Before you split a prompt, weigh the tradeoffs.

Reasons to keep a single prompt: lower latency (one round trip instead of many), lower cost (fewer tokens and calls), and simplicity (nothing to orchestrate). Modern long-context models can often handle in one call what used to require several, so start simple and only decompose when you have evidence you need to.

Reasons to chain: the task has genuinely distinct subtasks, you need to inspect or validate intermediate results, one step needs a tool or retrieval, or a single mega-prompt is unreliable and hard to debug. Chaining trades latency and cost for reliability and controllability — usually a good trade for anything user-facing or high-stakes.

How to choose an AI agent builder

When you evaluate an AI agent builder or prompt orchestration tool, run through this checklist against your team's real needs:

• Visual vs. code: Do you need a drag-and-drop canvas for faster iteration and non-engineer collaboration, a code-first framework for maximum control, or both? A tool that offers a visual builder and an SDK covers both audiences.
• Versioning: Can you version prompts, workflow structure, and tools — and roll back a bad change? Versioning is what makes production changes safe.
• Prompt management integration: Does each step pull from a shared, versioned prompt registry, or are prompts hard-coded and scattered across the codebase?
• Evaluations: Can you score changes against datasets and catch regressions before they ship, rather than eyeballing outputs?
• Observability: Does it log every step, input, output, latency, and cost in production so you can debug real failures?
• Tool support and testing: Can steps call tools and APIs, and can you mock those tools to test behavior end to end without live integrations?
• Deployment: How do agents move from build to production — versioned releases, API access, and the ability to update prompts without redeploying code?

Frequently asked questions

How should teams manage prompt chains, test prompt variations, and measure performance at scale? As prompt chains grow more complex, failures often emerge from interactions between steps rather than individual prompts. Teams manage this by testing chains end-to-end on representative datasets and evaluating overall outcomes. In practice, this is enabled through a prompt management platform that supports workflow orchestration, step-level tracing, and evaluation, allowing teams to perform root-cause analysis and tune performance at scale.

How do we handle branching logic and conditional paths inside chains? Branching logic becomes risky when prompt chains rely on intermediate outputs to control downstream behavior. Teams address this by defining explicit conditional paths and treating routing as part of the workflow itself. Tracing intermediate inputs and outputs makes it clear which path executed, enabling root-cause analysis and performance tuning as chains grow more complex.

How do we manage state across multi-step chains without hallucination drift? Teams manage state across multi-step chains by explicitly controlling what context persists between steps. Rather than passing full histories forward, they summarize or selectively propagate only relevant intermediate outputs. This scoped context management keeps chains within reliable context limits, reduces token usage, and prevents hallucination drift as workflows become longer and more complex.

How do we support multi-model chains? Supporting multi-model chains requires separating workflow logic from individual model integrations. Teams typically use a prompt management platform to abstract the LLM API layer, allowing different steps to call different models based on cost, latency, or quality needs. This enables flexible optimization without hard-coding provider choices into the workflow.

How do we design chains so they are modular, reusable, and composable? Chains become hard to maintain when prompt logic is duplicated across workflows. Teams design modular chains by treating prompts and sub-workflows as reusable, versioned components or snippets. This allows larger workflows to be composed from tested building blocks, making changes safer and ensuring consistent behavior across applications as systems scale.

What is prompt chaining? Prompt chaining is breaking a task into a sequence of LLM calls where each step's output feeds the next. Rather than one large prompt doing everything, you split the work into focused subtasks — for example, one prompt extracts key quotes from a document and a second prompt uses them to answer a question. Chaining improves reliability, transparency, and controllability, and is the foundation of most production LLM workflows.

What is the difference between prompt chaining and AI agents? Prompt chains are fixed pipelines: you define the steps in advance and the control flow is deterministic, which makes them predictable and easy to test. AI agents add a decision loop — the model chooses which tool or step to run next at runtime and loops until a goal is met. Agents are more flexible but harder to make reliable. Most production systems are chains with light branching rather than fully autonomous agents.

What is the best AI agent builder? It depends on your team. Code-first frameworks like LangChain/LangGraph and CrewAI give engineers maximum control; visual tools like n8n, Flowise, and Dify get pipelines running fast. The gap most leave is the operational layer. PromptLayer pairs a visual agent builder with a versioned prompt registry, evaluations, and observability in one platform, and stays full-featured for engineers through its SDK and API.

When should I use prompt chaining vs. a single prompt? Start with the simplest single prompt that could work — it has lower latency, lower cost, and nothing to orchestrate. Chain prompts when the task has distinct subtasks, you need to inspect or validate intermediate results, a step needs a tool or retrieval, or a single mega-prompt proves unreliable. Let evaluations show you where the single prompt falls down, and chain only those steps.