Handling Retry Logic with LLM (Large Language Model) in C++

In today's blog, we are going to explore how to implement a retry mechanism for handling interactions with a large language model (LLM) in C++. Specifically, we'll focus on a task that attempts to generate a valid response by retrying multiple times with feedback-based adjustments. This pattern is useful when working with LLMs, where we want to ensure that the generated content is both accurate and relevant, even in cases of failures or suboptimal responses.

The llm_task Function

The core function we're discussing is llm_task, which tries to generate a response using a prompt, checks the response, and retries up to a specified limit if the response is not acceptable.

Here’s the basic structure of the llm_task function:

llm_task(prompter, args, checker, feedbacker, retry_max) {
    i = 0;
    feedback = feedbacker();
    
    while (i < retry_max) { 
        llm_resp = llm(prompter(args, feedback));
        ret, check_resp = checker(i, llm_resp, feedback);
        
        if (ret == 0) {
            return 0, llm_resp;
        }
        
        feedback = feedbacker(check_resp);
        i += 1;
    }
    
    return 1, null;
}

Explanation:

• prompter: This function generates a prompt using the args and the feedback. The prompt is used to communicate with the LLM.
• args: The arguments that will be passed to the prompter.
• checker: This function checks whether the LLM’s response meets the desired criteria (e.g., length, relevance). It returns a status code (ret) and feedback for further iteration.
• feedbacker: This function updates the feedback based on the checker’s response.
• retry_max: The maximum number of retry attempts if the LLM does not generate a valid response.

The llm_task function loops up to retry_max times, each time modifying the feedback, until a valid response is generated.

Example 1: Simple Prompt-Check Cycle

Here’s an example where we try to generate a simple response based on a basic prompt and check the length of the response:

// Define the prompter, checker, feedbacker, and retry_max for this example
prompter = [](args, feedback) {
    return "Generate a response based on args and feedback: " + args + " " + feedback;
};

checker = [](i, llm_resp, feedback) {
    // Simple check: return 0 if response length is above a certain threshold, else return 1
    if (llm_resp.length() > 10) {
        return 0, "Response is valid.";
    }
    return 1, "Response too short.";
};

feedbacker = []() {
    return "Initial feedback.";
};

// Call the task
int retry_max = 3;
int status;
std::string response;
status, response = llm_task(prompter, "Request", checker, feedbacker, retry_max);

if (status == 0) {
    std::cout << "Valid response: " << response << std::endl;
} else {
    std::cout << "Failed to generate a valid response." << std::endl;
}

In this example, the llm_task function tries up to three times to generate a response. If the response length is greater than 10 characters, it is considered valid.

Output Example:

Valid response: Generate a response based on args and feedback: Request Initial feedback.

Example 2: Retry Mechanism with Modified Feedback

In this example, the feedback changes based on the number of attempts. The response must meet different criteria depending on whether it’s the first attempt or a retry:

// Define the prompter, checker, feedbacker, and retry_max for this example
prompter = [](args, feedback) {
    return "Attempting with args: " + args + " and feedback: " + feedback;
};

checker = [](i, llm_resp, feedback) {
    // Retry check based on attempt count (e.g., 1st retry allows shorter response)
    if (i == 0 && llm_resp.length() > 5) {
        return 0, "Valid response on first attempt.";
    } else if (i > 0 && llm_resp.length() > 10) {
        return 0, "Valid response after retry.";
    }
    return 1, "Response too short, need more retries.";
};

feedbacker = [](check_resp) {
    // Modify feedback based on check response
    return check_resp == "Response too short, need more retries." ? "Try harder!" : "Good job!";
};

// Call the task
int retry_max = 3;
int status;
std::string response;
status, response = llm_task(prompter, "Initial Request", checker, feedbacker, retry_max);

if (status == 0) {
    std::cout << "Final valid response: " << response << std::endl;
} else {
    std::cout << "Failed after retries." << std::endl;
}

Output Example:

Final valid response: Attempting with args: Initial Request and feedback: Good job!

In this case, the feedback changes after each check, guiding the LLM to generate better responses over time.

Conclusion

By using a retry mechanism combined with dynamic feedback and checking, we can improve the quality and reliability of responses from a large language model (LLM). Whether you’re developing a chatbot, an assistant, or any AI-driven application, this approach ensures that you handle imperfect or incomplete responses gracefully, retrying when necessary to produce the desired output.

We hope this blog post helps you understand how to implement retry logic with LLMs in C++. Feel free to experiment with the examples and adjust them to fit your use cases!

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