DeepLearningAI - AI Dev 25 | Aman Khan: Beyond Vibe Checks—Rethinking How We Evaluate AI Agent Performance
Aman from Arise introduces the concept of 'thrive coding,' which involves using metrics and data to evaluate AI agents, as opposed to 'vibe coding,' which relies on subjective judgment. The presentation highlights the use of open-source tools, particularly Phoenix, to help developers understand and evaluate AI applications. Phoenix allows for the visualization of application data, enabling developers to trace and evaluate the performance of AI agents. The session includes practical examples of using Phoenix to evaluate AI agents, focusing on ensuring the correct tools are called and improving prompt optimization through various techniques like few-shot prompting and meta prompting. The goal is to provide developers with tools to iterate and improve their AI systems effectively.
Key Points:
- Use data-driven 'thrive coding' instead of subjective 'vibe coding' for evaluating AI agents.
- Phoenix is an open-source tool that helps visualize and evaluate AI application data.
- Key components of AI agents include routers, skills, and memory, which need to be evaluated for efficiency.
- Prompt optimization techniques like few-shot prompting and meta prompting can improve AI performance.
- LLM as a judge can be used to evaluate AI outputs, ensuring correct tool usage and improving accuracy.
Details:
1. 🎤 Introduction: Setting the Stage
- The introduction effectively engages the audience and establishes a welcoming tone, crucial for setting the stage for the topics to follow.
- While this segment is primarily about preparing the audience, it does not include specific actionable insights or concrete metrics, focusing instead on context and engagement.
- This section is essential for providing a foundational understanding, though it lacks in-depth data-driven insights, serving more as a prelude to the main discussion.
2. 📚 Deep Dive into AI Evaluation
- Aman introduces himself as part of a collaboration with Deep Learning AI, focusing on evaluating AI agents, highlighting the significance of this partnership in advancing AI evaluation methodologies.
- He works at Arise, which plays a critical role in aiding large tech companies with the development and evaluation of AI applications and agents, showcasing their expertise in the field.
- The discussion involves open-source tools, such as Phoenix, which are accessible for public use and contribution, emphasizing their role in democratizing AI evaluation.
- The presentation's structure, with a quick overview of tools in the first half, suggests a practical approach, aiming to provide actionable insights and applications.
3. 🚀 Transitioning from Vibe to Thrive Coding
3.1. Emphasizing Data-Driven Decision-Making
3.2. Scalability Through Data
3.3. Promoting Consistency in Coding Practices
4. 🔍 Core Components of AI Agents
- AI agents are structured around three main components: input interface, reasoning/memory, and tool/API calls, forming the backbone of their functionality.
- The router component is crucial for reasoning and decision-making, determining necessary follow-up questions to refine user queries.
- Skills or execution involve the identification and execution of specific API calls or logic chains, which are essential for delivering targeted responses.
- The memory state component stores data required for interactions, allowing the agent to recall user information from previous sessions, thereby enhancing personalization.
- Customizable logic structures for tool access, such as LLM or API calls, are pivotal in tailoring the agent’s responses and improving its effectiveness.
5. 🛠️ Constructing and Evaluating AI Agents
- Evaluation of AI agents involves determining if the agent used the correct reasoning and logic for skill selection, utilizing ground truth labels for assessment. This ensures reliability in decision-making processes.
- Evaluation of a router focuses on whether the right skills were used to perform a task, with incorrect skill selection, such as wrong parameter extraction, serving as evaluative data. This highlights potential areas for improvement in task routing.
- Constructing skills involves assembling multiple API calls or tools, such as embedding a user query, performing a vector DB lookup, and retrieving context in LLM calls, into function calls. This process is essential for building complex AI functionalities.
- Efficiency in task completion is measured by the number of steps an agent takes to solve a problem, with excessive information requests indicating inefficiency. Streamlining these steps can significantly enhance performance.
- Visualizing information from notebooks using tools like Phoenix provides developers insights into application performance, with options for self-hosting or using cloud instances. This visualization is crucial for identifying performance bottlenecks and optimizing AI systems.
6. 🔧 Hands-On with AI Tools and Visualization
- The process begins with setting up the environment by importing essential packages and configuring a Phoenix collector endpoint with an API key for data collection.
- Instrumentation is key, as it involves creating detailed logs from AI application calls, enabling visualization of data in the Phoenix UI, similar to monitoring tools like Data Dog.
- The task includes testing a chat completion feature, with results displayed in the Phoenix UI to track metrics such as token usage and latency, which are critical for performance evaluation.
- Building the AI agent involves employing a router to strategically evaluate and route tool calls, ensuring the correct tools are selected for each specific query.
- A SQL generation tool is used to convert natural language questions into SQL queries, enhancing data retrieval efficiency.
- The data analysis tool interprets the SQL data to answer user queries, ensuring precise and actionable insights are provided.
- A data visualization tool is leveraged to create charts, aiding in the visual interpretation of data, with the AI agent deciding the necessity based on task requirements.
- Efficiency is evaluated by the agent's ability to determine when visualizations, like charts, are necessary, ensuring optimal resource use and task relevance.
7. 🧪 Evaluating Tool Calls and Iterating
- The evaluation process uses an agent for handling tool calls and executing SQL queries, ensuring traceability and reproducibility of each step, critical for accurate assessment and debugging.
- Attempts at visualization failed, as the agent returned a placeholder instead of actual visuals, underlining the need for rigorous testing and validation steps in the development pipeline.
- Leveraging LLM as a judge provides a structured evaluation framework, allowing systematic checks for correctness in LLM output, which is crucial for identifying tool call failures effectively.
- Evaluation metrics show that tool calls were successfully executed 80% of the time, highlighting a significant opportunity for enhancing precision and reducing errors.
- Logging evaluations systematically helps track tool call failures, which is instrumental in identifying patterns and guiding iterative improvements to enhance overall system reliability and performance.
8. 📈 Techniques for Prompt Optimization
- Gradient prompt optimization uses embeddings to tune the loss function, enhancing prompt effectiveness, particularly for complex prompts, by creating precise embeddings.
- Synthetic data generated with a large language model (LLM) was used to create a central 'golden dataset' for consistent versioning and evaluation.
- Baseline prompt evaluation initially showed 68% accuracy, suggesting significant potential for enhancement.
- Few-shot prompting, which involves integrating a few examples directly into the prompt, expands the context window and enhances determinism.
- The application of few-shot prompting raised accuracy from 68% to 84% by embedding three or four example rows, showcasing notable improvement.