This guide offers a structured approach to evaluating and optimizing the integration of third-party Large Language Models (LLMs) into applications, ensuring alignment with business goals and user needs through detailed checklists and evaluation metrics.

Evaluation guidelines for LLM applications

Improving AI models requires collecting accurate user feedback,which can be difficult. The article explores more into the importance of collecting human feedback and discusses the need for creating an organized database to effectively store and use this valuable input. We hope to continuously improve our large language models, ensuring they deliver greater performance and match user expectations more effectively.

Design feedback mechanism for LLM applications

When Large Language Models (LLMs) becomes popular and an essential tool for growing businesses, it signals a transition toward more complex and efficient data processing. Instead of outputting raw text, the models may now generate structured data in formats such as JSON or XML. This allowed the information to be directly integrated into company databases, removing the need for human processing. Businesses can use structured outputs to streamline their data workflows, decrease processing time, and improve the accuracy and reliability of their analytics.

History of Structured Outputs for LLMs

Hybrid search comes to solving problems in vector search and full-text search. Let's see how it works. In this article, we will know how hybrid search approach and how to evaluate each search method through metrics in information retrieval.

Evaluating search engine in RAG systems

Logs are like the footprints of your LLM, tracking every move it makes. We will look at how logging can help you see beneath the top layer of a system, which can help you troubleshoot problems and better understand the system behavior.

Logging

Metrics give you the rundown on how your LLM’s performing. We will show how to use these metrics to identify issues, increase efficiency, and make changes for improved outcomes.

Metrics

Explore Anthropic's Model Context Protocol (MCP) - a groundbreaking approach to enhancing AI assistants. Learn how MCP integrates with local and remote resources, enabling smarter interactions with tools and databases.

Intro to Model Context Protocol

Observability in AI is all about understanding what’s going on inside complex systems. It gives you the tools - logs, metrics, and traces - to monitor, troubleshoot, and optimize how AI models and services run.

Observability in AI platforms

The article talks about how important human feedback is for making large language models better, especially using thumbs up and down. It explains the Pearson correlation coefficient, which measures how variables are related. The thumbs system is an easy way for users to show if responses are helpful or not, which encourages more feedback and keeps users engaged. This feedback helps improve training data and makes the models more accurate. The article includes examples in sentiment analysis for reviews and content moderation, showing how this feedback helps understand user opinions and filter out bad content on sites like YouTube and Twitter.

Thumbs up and Thumbs down pattern

Tracing is like following your LLM’s journey, step by step. We will explain how tracing makes it easy to identify and address problems by allowing you to track the entire process.

Tracing

Stay updated on the latest in AI tools for developers with this week’s digest, featuring Cline's v2.0.0 update, OpenAI’s Realtime API, and Aider’s Architect/Editor split for enhanced coding workflows.

AI digest #1 Aider reasoning, OpenAI Realtime API, Cline - pre Claude-dev 

Stay updated on the latest in AI tools for developers with this week’s digest, new command Aider, OpenHands, Qwen2.5 Coder 32B

AI digest #2 New command Aider, OpenHands, Qwen2.5 Coder 32B, Predicted Output

Salesforce is taking AI to the next level with large language models that make customer service smoother, sales more strategic, and insights faster. By automating routine tasks, these tools free up teams to focus on real connections with customers. The result? Happier customers, smarter sales, and big wins for businesses.

Salesforce use cases

Yelp already had a machine learning platform before the big push for large language models (LLMs). Now, they’re using LLMs to level up their search and recommendation systems, making it easier for moderators and businesses to track down users. Let’s dive into how Yelp is making it work.

datnguyennnx

Contributions