The LLM fan-out pattern is a technique for improving the reliability and accuracy of large language model (LLM) responses by generating multiple answers to a single query and then consolidating them into a single, more robust result. Instead of asking a question once and hoping for the best, you ask it multiple times in parallel and use the collection of responses to identify the most likely correct answer.

Think of it like asking a panel of experts for their opinion instead of just one. If nine out of ten experts agree, you can be much more confident in their conclusion. The fan-out pattern applies this same logic to LLMs, creating a virtual panel of experts to tackle complex or high-stakes tasks.

The process is straightforward but powerful. You send a single initial prompt to an orchestrator, which then “fans out” by sending multiple, simultaneous requests to one or more LLMs. Once the LLMs return their individual responses, an aggregator uses a voting mechanism to determine the best final answer.

This approach helps mitigate common LLM weaknesses like “hallucinations” (fabricating information) or sensitivity to specific prompt wording. By sampling a variety of responses, you can filter out outliers and converge on a more factually accurate and consistent result.

There are three primary tactics for implementing the fan-out pattern:

• Self-consistency sampling: Querying the same model with the same prompt multiple times to explore different reasoning paths.
• Prompt ensembles: Querying a model with several variations of the same prompt to see if the answer remains consistent.
• Consensus and voting: Applying rules, from simple majority votes to more complex scoring, to aggregate the results and select a winner.

These tactics combine to make LLM outputs more dependable, especially for tasks that require deep reasoning or have a low tolerance for error.

The fan-out pattern is most valuable when the cost of an incorrect answer is high. While it adds latency and computational cost, the investment pays off in quality and reliability.

• Complex reasoning tasks: For multi-step math problems, logic puzzles, or code generation, fan-out allows the model to explore multiple paths to a solution. The most common answer is often the most logically sound.
• High-stakes analysis: In fields like finance, law, or medicine, fan-out can be used to summarize complex documents or analyze data where precision is critical. Getting a consensus view reduces the risk of a single model’s error leading to a bad decision.
• Reducing factual hallucinations: When asking for specific facts, dates, or figures, running multiple queries can help identify and discard responses where the model fabricates information. If one response differs significantly from the others, it’s likely a hallucination.
• Creative ideation: Fan-out isn’t just for analytical tasks. You can use it to generate a diverse set of creative ideas, marketing copy, or design concepts, then pick the best one or combine elements from several.

While the core idea is simple, its implementation can vary. The three main tactics—self-consistency, prompt ensembles, and voting—can be used alone or in combination.

This tactic leverages the probabilistic nature of LLMs. By increasing the “temperature” setting of a model, you make its output more random and creative. Self-consistency works by running the same prompt through the same model multiple times with a higher temperature.

This encourages the model to generate different “chains of thought” or reasoning paths. Even though the paths are different, if most of them arrive at the same final answer, you can have high confidence in that answer.

Example Starter Prompt:

Q: There are 15 trees in the grove. The grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?

A: Let’s think step by step.

By asking the model to show its work, you get insight into its reasoning. When you run this prompt five times, you might get five different explanations, but four of them will likely conclude with the answer “6”.

LLMs can be highly sensitive to the way a question is phrased. A slight change in wording can sometimes produce a dramatically different answer. The prompt ensemble method is designed to counteract this.

Instead of sending the same prompt every time, you create a set (an “ensemble”) of semantically similar prompts.

Example Prompt Variations:

• “Summarize the attached article into three key bullet points.”
• “Provide a three-point summary of the main arguments in this text.”
• “What are the three most important takeaways from the document provided?”

By running these variations, you can check if the model’s core understanding remains stable. If all prompts yield similar summaries, the result is likely reliable.

Once you have a set of responses from either self-consistency or prompt ensembles, you need a way to choose the best one. This is where voting comes in.

• Simple Majority Vote: This is the most common method. For quantifiable answers (like a number or a multiple-choice option), you simply count the votes and the answer with the most votes wins.
• Consensus Mechanisms: For more complex, open-ended responses like summaries or code blocks, a simple vote won’t work. Instead, you can use another LLM call to compare the responses and select the one that best represents the consensus or is of the highest quality.

Example Consensus Prompt:

You are provided with five different summaries of a legal document. Your task is to act as an expert reviewer. Analyze all five summaries and select the one that is the most accurate, comprehensive, and clearly written. Explain your reasoning for choosing the winning summary.

This turns the aggregation step into its own intelligent process, ensuring the final output is coherent and high-quality.

The fan-out pattern isn’t a silver bullet. Its primary drawbacks are increased cost and latency—you’re making multiple API calls for what is functionally a single query. This makes it unsuitable for certain applications.

Avoid using the fan-out pattern for:

• Real-time applications: Chatbots or other interactive tools where users expect instant responses cannot afford the latency introduced by multiple parallel calls.
• Low-stakes tasks: If the impact of an occasional error is low (e.g., generating alt text for images), the additional cost is likely not justified.
• Simple data retrieval: For tasks that are more about lookup than reasoning, a single, well-crafted prompt is usually sufficient.

The key is to apply it judiciously where the trade-off between cost and reliability makes sense for your product and your users.

Implementing a pattern like LLM fan-out requires a robust system to manage the flow of data, execute custom logic, and handle API calls. This is where an engine for managing user-centric workflows becomes essential.

Kinde provides a powerful workflow engine that can act as the orchestrator for complex computational patterns. You can use Kinde to trigger a fan-out process based on a user action, like a button click or a form submission. The workflow can then execute custom TypeScript code to make parallel calls to an LLM API, collect the responses, and perform a consensus vote to determine the final answer.

By using Kinde to handle the underlying infrastructure, you can focus on perfecting the logic of your fan-out strategy instead of building and maintaining a complex orchestration system from scratch.

• About workflows
• Create a workflow
• Workflow examples