How can I reduce API call costs when building a ChatGPT-like bot?

Below are some best practices that many developers use to reduce the token count (and therefore cost) when building a ChatGPT-like bot:

1. Summarize Conversation History

Instead of sending the full transcript of every previous message, create a summary of past interactions. For instance, maintain a running summary that captures the key details and context. When making API calls, send:

• The current user prompt.
• The summarized history.
• Any necessary system instructions.

2. Use Role-Based Prompts Efficiently

Include role-specific messages (system, user, assistant) only when they add value. Remove redundant messages so that you only include what's absolutely necessary for the current context.

3. Limit Conversation Context Length

Decide on a fixed context window size (e.g., last several messages or tokens) and prune older or less relevant messages. You might:

• Keep a sliding window of recent interactions.
• Archive older parts of the conversation summary that are less critical.

4. Fine-Tune Your System Prompts

Rather than including lengthy system prompts every time, consider storing essential instructions server-side or embedding them into your summarization so that each new API call doesn’t have to reintroduce all the context.

5. Use Caching and Reuse

For interactions that repeat frequently, cache previous responses or context summaries. If the same prompt leads to a similar conversation state, reuse past summaries to cut down on token usage.

6. Optimize for Tokens

Ask the model for shorter outputs when appropriate, and be mindful of token budgeting. When formatting your prompt, choose concise language that conveys the necessary details without extra words.

Example Workflow

Below is an outline of a typical workflow:

PYTHONCopied
def create_api_payload(user_message, conversation_history, system_prompt):
    # Summarize the conversation history if it's too long
    summary = summarize_history(conversation_history)
    
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": summary},
        {"role": "user", "content": user_message},
    ]
    return {"messages": messages}

def summarize_history(history):
    # Implement a summarization algorithm or use a shorter version of history
    # This is just an example placeholder
    summary = "Summary of previous conversation: " + " ".join(history[-5:])
    return summary

By maintaining a concise context with summaries and by managing how much text each API call sends, you can reduce the token count per call and, consequently, lower the overall cost.

These strategies help keep your token usage efficient while still preserving the necessary context for coherent interactions.