Introduction to Building with LLMs in Xano

Welcome to this guide on building with Large Language Models (LLMs) in Xano! In this tutorial, we'll explore how to evaluate different LLMs, connect to them via APIs within Xano's function stack, and optimize your queries to get the desired output. Let's dive in!

Understanding LLMs

Before we begin, let's define some key concepts related to LLMs:

• Large Language Models (LLMs) are trained on large amounts of general public data, such as the internet and other public datasets. However, they don't have access to private knowledge or documents specific to your business.
• LLMs convert text into tokens and look for patterns within those tokens to predict the next word in a sequence, creating human-like responses.
• LLMs operate on a cost model based on tokens, charging you per token for input and output.
• LLMs have a context window, which is the amount of information (in tokens) they can maintain coherence across. This window can range from 4,000 tokens to over a million tokens.
• LLMs also have output limits, meaning they can only generate a certain number of tokens (roughly 8-16 Google Docs pages) in a single API request.

Evaluating LLMs

When evaluating LLMs, consider the following factors:

1. Cost: Analyze the cost per API request and for your entire project. Open-source models like LLAMA3 can be significantly more cost-effective than proprietary models like GPT-4.
2. Speed: Smaller models tend to respond faster, which is crucial for real-time applications like chatbots.
3. Privacy: If you have sensitive data or compliance requirements, you may need to self-host the LLM within your environment.
4. Capabilities: Consider the context window size, input modalities (text, images, video), and whether you need to fine-tune the model for specific tasks.
5. Extensibility: Some models allow fine-tuning, which can improve performance and reduce costs for specific use cases.
6. Language Support: Most LLMs are trained primarily on English data, so consider language-specific models if you need to work with other languages.

Connecting to LLMs in Xano

To connect to an LLM in Xano's function stack, follow these steps:

1. Copy the API request example from the LLM provider's documentation (e.g., OpenAI's documentation for GPT-4).
2. In the Xano function stack, create a new block and import the API request curl command. Xano will automatically format the request for you.
3. Inspect the request body, which typically includes the model name, messages (the input text), and other parameters specific to the LLM provider.
4. Customize the request body based on your requirements, such as changing the input text or adjusting settings like temperature or max tokens.
5. Execute the function to send the API request and retrieve the LLM's response.

Improving API Requests for Better Responses

To get better responses from the LLM, you can optimize your API requests in several ways:

1. Provide context: Include relevant background information or examples to help the LLM understand the context of your query.
2. Refine prompts: Experiment with different prompts and instructions to guide the LLM towards the desired output.
3. Adjust parameters: Tweak settings like temperature (randomness), max tokens (output length), and stop sequences to influence the LLM's response.
4. Iterate: Send multiple requests, analyzing the responses and refining your prompts and parameters until you achieve the desired output.

Optimizing LLM Queries

Even with the best prompts and parameters, you may still encounter issues like hallucinations (incorrect responses), lack of coherence across long contexts, or inconsistent responses. In such cases, follow these steps to optimize your LLM queries:

1. Break down complex tasks: Instead of sending a single, lengthy request, break down your task into smaller, more manageable steps.
2. Use intermediate steps: Generate intermediate outputs and use them as input for subsequent requests, building up to the final desired output.
3. Leverage external tools: Integrate external tools or libraries to preprocess your input data or post-process the LLM's output.
4. Fine-tune the model: If available, fine-tune the LLM on your specific domain or task to improve its performance and reduce costs.

By following these steps and continually iterating, you can optimize your LLM queries and achieve reliable, desired outputs for your Xano applications.

Remember, Xano's function stack and its integration capabilities make it easy to connect to and work with LLMs via APIs, enabling you to leverage the power of these cutting-edge language models without writing code.