Prompt technique to skyrocket Claude’s accuracy 🚀

Today’s top AI Highlights:

1. Anthropic’s Simple Technique for Improved Sentence-Level Retrieval

2. Multimodal RAG templates by LangChain

3. Production-ready Reinforcement Learning AI Agent

4. Meta’s Opensource Tools for AI Safety and Evaluation

5. Use AI to Take Notes While You Listen

& so much more!

Read time: 3 mins

Latest Developments 🌍

Understanding the Needle in a Haystack 🪡

Claude 2.1 with its humongous context window of 200k tokens, excels in real-world retrieval tasks across long contexts and has been trained on large amounts of feedback on long document tasks. However, the model sometimes shows reluctance to answer questions based on an individual sentence, especially if that sentence seems out of place in a document. But a simple technique can address this issue.

Key Highlights:

1. During an evaluation, Claude 2.1 was presented with a sentence “The best thing to do in San Francisco is eat a sandwich and sit in Dolores Park on a sunny day.” from a long document about startups. When asked a question based on that sentence, the model chose not to answer.

2. The model's reluctance to answer in certain scenarios is attributed to its training. It has been conditioned not to respond if the document doesn't contain enough relevant information, to prevent incorrect or misleading answers.

3. Thereby, a simple yet effective technique was used. By initiating the model's response with “Here is the most relevant sentence in the context:”, the model is directed to look for relevant sentences first. The prompt overrides Claude’s reluctance to answer based on a single sentence, enhancing its retreival and accuracy.

Multimodal RAG Templates by LangChain

Slide decks are a powerhouse of knowledge, yet their visual elements are out of reach for most of the RAG applications. Enter Multi-modal LLMs like GPT-4V that can enable Q+A assistants to interact seamlessly with the visual content of slides. LangChain just released a new template to easily set up multi-modal RAG apps.

Here's a step-by-step example of building a RAG app for an investor presentation by DataDog:

1. Step 1: Convert the slide decks into a series of images, embedding using OpenCLIP's advanced multi-modal embeddings. Store them securely in Chroma's vectorstore for easy access.

2. Step 2: Launch into an immersive, multi-modal chat app experience, specifically tailored for navigating through the slide deck. It comes equipped with an interactive chat playground, ready to engage.

3. The app smartly retrieves the most relevant slide image using its multi-modal embeddings, ensuring you get exactly what you need. Observe the image below, particularly the LangSmith trace on the right side: this showcases the slide selected in response to a user query. This chosen slide is then processed by GPT-4V to formulate a response, as demonstrated in the chat playground on the left.

Production-ready Reinforcement Learning AI Agent 🤖

The existing RL systems often fall short in handling complex challenges like delayed rewards, partial observability, and the balance between exploration and exploitation. Meta has open-sourced PEARL, a comprehensive RL agent software package that not only addresses these gaps but also brings a nuanced approach to RL applications, designed for practical, real-world scenarios.

Key Highlights:

1. PEARL integrates five key modules: policy learner, exploration module, history summarization module, safety module, and replay buffer. This integration allows Pearl to efficiently tackle issues like partial observability and delayed rewards, offering a more holistic solution.

2. PEARL supports diverse learning algorithms and exploration strategies, ensuring adaptability across various RL applications. Its safety module further strengthens its application in scenarios where risk management and constraint adherence are crucial.

3. Pearl has been successfully implemented in various industry applications, including auction-based recommender systems and ads auction bidding. The software has undergone rigorous benchmarks across discrete control tasks, contextual bandit benchmarks, and versatile agent performance assessments, demonstrating its effectiveness and adaptability across a range of RL challenges.

Open-Source Path in AI Trust and Safety too 🤝

Meta has announced Purple Llama, an open comprehensive project designed to enhance trust and safety in the rapidly evolving domain of generative AI. Purple Llama encompasses an array of open-source tools and evaluations, specifically tailored to assist developers in responsibly implementing generative AI models.

Under the umbrella of Purple Llama, Meta has introduced two key tools: CyberSec Eval and Llama Guard.

1. CyberSec Eval serves as a cybersecurity safety evaluation benchmark for LLMs, grounded in industry standards and guidance. It is intended to help quantify the cybersecurity risks associated with LLMs, such as evaluating the propensity of AI models to suggest insecure code.

2. Llama Guard, on the other hand, is a safety classifier designed for input/output filtering. Its primary function is to prevent the generation of risky outputs from AI models. It leverages a combination of publicly available datasets for its training, aiming to provide an easily deployable solution for AI safety.

Tools of the Trade ⚒️

• LazyNotes: AI note-taking app that allows you to listen and take notes simultaneously, and helps improve eye contact and rapport instead of focusing on note-taking. The notes can be shared easily and summarized later for more analysis.

• Artie's Analytics Portal: An AI-powered platform offering real-time observability and efficient data syncing for database pipelines using change data capture and stream processing.

• Algomax: A platform designed to streamline LLM and RAG apps evaluation, that integrates smoothly into existing pipelines, providing detailed insights and comprehensive metrics through an intuitive dashboard.

• Monoid: Transform APIs into "Actions" that can be utilized by AI agents. It allows you to create AI agents by selecting a foundational LLM, an agent type, and various actions. This enhances the LLM's ability to understand context and act on behalf of users.

Hot Takes 🔥

1. I don't even slightly expect this to happen, but imagine if 5 months ago GPT4 was the pinnacle of AI. And no one discovers how to get back there. That'd sure be weird. ~ shako

2. Today’s AI is the most primitive AI that you will see for the rest of your life. ~ Bojan Tunguz

3. We really don’t know anything about Gemini Ultra. Does it beat GPT-4 for real? If so, why by such a small amount? Two options: 1) Gemini represents the best effort by Google, and the failure to crush GPT-4 shows limits of LLMs approaching 2) Google’s goal was just to beat GPT-4 ~ Ethan Mollick

Meme of the Day 🤡

That’s all for today!

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