Best Practices for Handling Multi-Turn Conversations in LLMs: The Case for Merging Sequential Messages

In the world of large language models (LLMs), building conversational applications often involves managing multi-turn interactions where users and assistants exchange messages. However, a subtle but critical issue arises when your app’s logic generates multiple consecutive messages from the same role—either the user or the assistant. This can disrupt the model’s expected input format, leading to unexpected behaviors like hallucinations, incoherent responses, or even API errors from certain providers.

While this isn’t always named as a best practice in provider docs, it’s clearly implied or required by providers like Anthropic and encoded in popular SDKs like LangChain. Their API guides and sample code almost always showcase strict user-assistant alternation, reflecting how these models were fine-tuned. In this post, we’ll formalize this practice, explain why it’s essential, and explore implementation strategies. We’ll also discuss alternatives for scenarios where non-alternating formats are unavoidable, such as through fine-tuning.

Why Merge Sequential Messages?

LLMs like GPT series, Claude, or Gemini are predominantly trained and fine-tuned on datasets featuring alternating turns: a user message followed by an assistant response, and so on. When you feed them a history with back-to-back messages from the same role, it can confuse the model, as it deviates from this pattern. Here’s why merging—combining consecutive messages of the same role into a single, cohesive entry—improves accuracy and reliability:

Alignment with Training Data

Most chat-tuned LLMs expect a rhythmic back-and-forth. Consecutive assistant messages might cause the model to “continue” its own thought instead of responding to the user, while multiple user messages could be interpreted as a fragmented query. Merging preserves the alternation, making the input more akin to the model’s training examples and reducing the risk of off-topic or erratic outputs.

Provider Compatibility and Error Prevention

Some SDKs and gateways may still error on non-alternating turns, but Claude’s API now auto-combines consecutive turns. For instance, GitHub issues and community posts highlight real errors when requests include consecutive assistant or user turns, especially with older SDKs or via services like AWS Bedrock. Even after updates, inconsistencies persist in certain integrations, leading to errors like “roles must alternate between ‘user’ and ‘assistant’.” Libraries such as LangChain address this by offering built-in utilities (e.g., mergeMessageRuns) to automatically handle merging, ensuring compatibility across different models.

Enhanced Context Management

In batch messaging setups—common in apps emulating natural conversations—unmerged histories can bloat the prompt with redundant role markers, wasting tokens and complicating the model’s focus. Merging condenses the input without losing information, which is crucial for long conversations where context windows are limited. This leads to more efficient token usage and better overall coherence.

Empirical Evidence from Practice

Developers report that merging stabilizes outputs in real-world applications. For example, in scenarios involving simulated multi-round interactions or asynchronous user inputs, unmerged histories often result in the LLM generating self-referential loops or ignoring parts of the context. By contrast, merged prompts yield more predictable and accurate responses, as evidenced in community discussions on forums like Reddit’s r/MachineLearning or Stack Overflow threads on LLM integrations.

How to Implement Message Merging

Implementing merging is straightforward and can be done in your app’s backend logic before passing the history to the LLM API. The goal is to scan the message array and concatenate content from the same role, using a separator like a newline for readability. However, refine the tool to filter for non-user/assistant roles (e.g., system or tool messages), handling them separately to avoid unintended merging.

Basic Algorithm

Initialize an empty list for the merged history.
Iterate through each message in the original history.
If the current message’s role is user or assistant and matches the last one in the merged list, append its content to that entry (e.g., with a newline separator).
For other roles (e.g., system, tool, or function), add them as-is without merging, as their IDs or structures often matter and shouldn’t be combined.
Otherwise, add it as a new entry.

This ensures the final history alternates roles perfectly while respecting special message types.

Example in Python

Here’s a simple function you can drop into your code, with added handling for non-user/assistant roles:

def merge_messages(history):
    merged = []
    for message in history:
        role = message['role']
        if role not in ['user', 'assistant']:
            # Handle non-user/assistant roles separately (e.g., system or tool) - do not merge
            merged.append(message.copy())
            continue
        if merged and merged[-1]['role'] == role:
            merged[-1]['content'] += "\n\n" + message['content']  # Use double newline for clear separation
        else:
            merged.append(message.copy())  # Copy to avoid modifying originals
    return merged

Apply this like so:

# Example history with consecutive assistant messages and a tool message
original_history = [
    {"role": "user", "content": "What's the weather?"},
    {"role": "assistant", "content": "It's sunny."},
    {"role": "assistant", "content": "And warm too."},
    {"role": "tool", "content": "Tool response here.", "tool_id": "123"},  # Not merged
    {"role": "user", "content": "Thanks!"}
]

merged_history = merge_messages(original_history)
# Result: Alternating user/assistant with combined assistant content; tool unchanged

Handling Edge Cases

Formatting Preservation: If messages include structured elements like lists or code blocks, use appropriate separators to avoid breaking them. For instance, add “—” between parts if needed.
Timestamps or Metadata: If your app tracks message timestamps, embed them in the content during merging (e.g., “[Timestamp: 2023-10-01] Original message…”).
Long Contexts: For very lengthy merged messages, consider summarizing older portions while keeping recent ones intact to stay within token limits.
Media or Rich Content: Merging works best for text; for images or other media, handle them separately or attach to the merged text entry.
Tool/Function Messages: As noted in LangChain docs, avoid merging these, as their unique IDs and structures (e.g., for tool calls) are critical and could break functionality if combined.
Testing: Always A/B test with your specific LLM. Measure metrics like response coherence, relevance scores, or user satisfaction to validate improvements.

Alternatives: Fine-Tuning for Non-Alternating Formats

While merging is a quick fix for most cases, sometimes your application demands non-alternating message flows—such as interleaved system prompts, multi-agent simulations, or custom role structures. In these scenarios, fine-tuning the model can adapt it to handle such inputs more gracefully.

Why Fine-Tune?

Base models or open-source alternatives (e.g., Llama or Mistral) can be fine-tuned on datasets that include non-alternating examples, teaching the LLM to process them without confusion. This is particularly useful for specialized apps where alternation isn’t feasible.

Steps to Fine-Tune

Prepare a Dataset: Curate examples with your desired message patterns. Use tools like Hugging Face’s Datasets library to format them. Include diverse scenarios to ensure robustness.
Choose a Base Model: Start with an open-source model like Meta’s Llama 3 or Mistral 7B, which are efficient for fine-tuning.
Fine-Tuning Frameworks: Leverage libraries such as Hugging Face Transformers, PEFT (Parameter-Efficient Fine-Tuning), or Unsloth for efficient training. For example:
- Use LoRA (Low-Rank Adaptation) to fine-tune only a subset of parameters, reducing compute needs.
- Train on a cloud platform like Google Colab, AWS SageMaker, or RunPod for GPU access.
Evaluation: Post-fine-tuning, test on held-out data focusing on non-alternating inputs. Metrics like perplexity, BLEU scores, or human evaluations can gauge success.
Deployment: Host the fine-tuned model via services like Together AI or Hugging Face Inference Endpoints for easy integration.

Keep in mind that fine-tuning requires resources (data, compute) and expertise. Importantly, fine-tuning can teach a model to handle atypical transcripts, but it doesn’t override provider API format rules (e.g., if an endpoint mandates alternation, your payload still has to comply). For example, Anthropic’s guidance emphasizes that models are trained on alternating turns, so even custom models may need payloads to align with API expectations. If your use case is niche, it might be worth it; otherwise, stick to merging for simplicity.

Providers like OpenAI offer fine-tuning APIs, but they still recommend alternating formats in base guidelines. For fully custom needs, explore community models on platforms like Hugging Face Hub that are already tuned for flexible conversation structures.

Conclusion

Merging sequential same-role messages is an under-discussed but powerful best practice for enhancing LLM performance in conversational apps. By aligning with how models are trained and documented (albeit implicitly), you can avoid common pitfalls and deliver more reliable experiences. If merging doesn’t fit your architecture, fine-tuning opens doors to greater flexibility.

If you’re building an LLM-powered app, give merging a try—it’s a low-cost tweak with potentially high rewards. Share your experiences in the comments below; I’d love to hear how it impacts your projects!