Lingua Custodia’s Post

“Shall we do RAG or fine-tuning?” I've been asked this question very often. One does not exclude the other. There is discussion whether Retrieval-Augmented Generation (RAG) or fine-tuning yield better results when deploying a solution based on Large Language Models (LLMs) The RAG pattern combines retrieval of companies'  information with generation of answers based on that information, through an LLM. This technique is a relatively low hanging fruit, and allows for up to date information. Fine-tuning means further training the model and adjust the LLM's weights to specific domains and tasks, enhancing performance on taking challenges. In both cases, and specially so in fine-tuning, the data pre-processing is an important step that shall not be overlooked. But, these techniques are NOT mutually exclusive and complement each other very well. For instance, a fine-tuned customer support chatbot can utilize RAG to incorporate the latest customer data, delivering contextual and personalized responses. We don't need to choose one over the other. If you need help on this topic, talk to us. #ai #largelanguagemodels #finetuning #rag

"Data is the fuel and lifeline of any #AI system" Today I am sharing an abstract yet simple example of an #AI system’s lifecycle. Developers and deployers of #artificialintelligence (AI) systems have a variety of distinct responsibilities for addressing risks throughout the lifecycle of the system. These responsibilities range from problem definition to #data collection, labelling, cleaning, #modeltraining and ‘fine-tuning’, through to testing and deployment as shown in this Figure. #datascience #artificialintelligence  #aimodels #datadeployment #adaption source: https://shorturl.at/DLcOq

In the realm of AI, two distinct approaches stand out for enhancing language models: RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation). RAG involves dynamically retrieving relevant information from external knowledge sources, such as a database or the web, during the generation process. This enables the model to access the latest information, leading to more precise and informative responses. On the other hand, CAG preloads all necessary data into the model's context window. This eliminates the need for real-time retrieval, making the generation process faster and more efficient. However, it necessitates careful consideration of the context window size and the volume of data to be preloaded. Here's a quick comparison between RAG and CAG: - Data Retrieval: - RAG: Dynamic retrieval during generation - CAG: Preloads all data into context - Speed: - RAG: Can be slower due to retrieval latency - CAG: Generally faster - Flexibility: - RAG: Can access a wider range of information - CAG: Limited to preloaded data - Context Window: - RAG: No strict limit - CAG: Limited by context window size When to Use: - RAG: Ideal for constantly evolving information, vast knowledge bases, or when accuracy is paramount. - CAG: Suited for relatively static knowledge bases, prioritizing speed and efficiency, or when the context window size accommodates all relevant data. Example Applications: - RAG: A chatbot providing real-time updates on news events. - CAG: A customer service bot offering details on a company's stable product catalog. Understanding the strengths and limitations of RAG and CAG helps in selecting the most suitable approach for your specific AI application. #RAG #CAG #MachineLearning #LLM #AI #genai

What is Multi token Prediction in LLMs? 📖 Imagine reading a book, but instead of reading one word at a time, you glance at a phrase or even a whole sentence. Your brain processes multiple words simultaneously, allowing you to understand the meaning more quickly and efficiently. 🤖 Multi-token prediction works similarly for AI language models. Traditionally, these models predict the next word based on the previous words, like reading a book one word at a time. With multi-token prediction, the model looks ahead and predicts multiple words at once, just like skimming a phrase or sentence ⚡ By processing larger chunks of text in one go, the AI can: > Generate text up to 3 times faster > Better understand and maintain context over longer distances > Improve overall performance, especially for large models It's like giving the AI a bigger piece of the puzzle to solve at once, enabling it to see the bigger picture and make more accurate predictions. This innovative approach has the potential to revolutionize various applications of language models, such as more efficient code completion, enhanced writing assistance, and improved language translation. What potential applications do you see for multi-token prediction in AI? Share your thoughts below! #MachineLearning #DataScience #AI #AIWhisperers #artificialintelligence #AIExplained #AIdevelopment #AIstrategy #AIInteractions #AILiteracy #EffectiveAI #LLM #chatgpt #DataScience #AIModels #DigitalTransformation For more cool AI stuff, check us out

LangChain vs. Retrieval-Augmented Generation (RAG): What’s the Difference? 🤔 In the rapidly evolving AI landscape, LangChain and Retrieval-Augmented Generation (RAG) are two powerful tools, each with its own strengths. But how do they differ, and which one is right for your AI projects? 🔗 LangChain: Focus: Specializes in chaining multiple language models and components to create complex workflows. Core Functionality: It’s all about modularity—combining different LLMs into a seamless pipeline for tasks like content generation, customer support, and data analysis. Use Cases: Ideal for building sophisticated applications that require multi-step processing, such as automated content creation and intelligent chatbots. 🔍 RAG: Focus: Enhances text generation by integrating real-time information retrieval. Core Functionality: Combines the power of generative models with retrieval systems to ensure accuracy and relevance in responses. Use Cases: Perfect for tasks where up-to-date, contextually relevant content is critical, like real-time Q&A systems and dynamic content creation. In a nutshell: * LangChain excels at structuring and managing complex workflows. * RAG shines in generating accurate and contextually relevant content by pulling in real-time data. Which one aligns with your needs? 🤖 #AI #ML #Technology #Innovation #LangChain #RAG #GPT #DataScience

ChainBuddy: An #AI System for #LLMPipeline Creation The paper introduces ChainBuddy, an AI agent system built into the ChainForge platform to help generate and evaluate LLM pipelines. It addresses the "blank page" problem for users by providing a structured and user-friendly interface to build, evaluate, and optimize LLM behavior for diverse tasks. #ChainBuddy reduces the workload and boosts user confidence in setting up evaluation pipelines, making #LLM application development more accessible and effective. For more details, read the full paper here https://lnkd.in/gyGmvBzs #AI #LLMs #RAG #DataScience #Data #Technews #TechnologyTrends

🤖 Demystifying AI Agents in a Few Simple Steps! 🤖 The spotlight on AI Agents is undeniable, especially in the latter part of the year, if not already. While Large Language Models (LLMs) are impressive, their utility extends beyond mere conversation or paraphrasing. They truly shine when they can take actionable steps and automate processes. However, several challenges hinder their seamless integration into real-world applications: 🤷♂️ Non-deterministic Nature of LLMs: For instance, rephrasing a user's request can lead to various action plans, influencing the final response. ✔️ Accuracy: Certain use cases demand an agent's accuracy to exceed 99% before unlocking their full potential. 📍 Execution: LLMs still grapple with executing tasks efficiently. 🤯 Short-term Memory Limitation: This constraint restricts the number of steps an agent can execute. Despite these challenges, we witness the deployment of agents across diverse industries and applications. Let's delve into an example to understand their functioning: 🤔 User Query: "How many orders did we complete successfully yesterday in City A?" 1️⃣ Planning: The agent strategizes how to proceed with the request. 2️⃣ Action: It identifies the relevant tables. 3️⃣ Observation: It determines the pertinent tables to utilize. 4️⃣ Thought: The agent queries the table schema to identify relevant columns. 5️⃣ Action: It identifies the relevant columns. 6️⃣ Observation: It confirms the pertinent columns. 7️⃣ Action: The agent generates an SQL query. 8️⃣ Tool: It executes the SQL tool with the generated query. 9️⃣ Observation: It retrieves the number of orders in City A. 🔟 Agent Answer In essence, this mirrors the process of learning coding—breaking down tasks into manageable steps. #Agents #AI #GeneralAI #HamzaAliKhalid #MoonSys

𝗛𝗼𝘄 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗔𝗜 𝗝𝗦𝗢𝗡 𝗼𝘂𝘁𝗽𝘂𝘁 𝗳𝗿𝗼𝗺 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 #𝗳𝗹𝗼𝘄𝗶𝘀𝗲 #𝗮𝗶 🔍 Unlocking the Power of Structured Output with AI! 🤖 Ever needed an output that's crystal clear and consistent? Chat models are excellent at conversation, but they can sometimes struggle with strict guidelines. Here’s where a structured output parser makes all the difference: - Chat Prompt Template: Takes in your input, one sentence at a time. - OpenAI Chat Model: Connects effortlessly to a simple LLM chain. - Structural Parser: Delivers sentiment analysis in an easy, Boolean format (true/false). Thanks to insights from Leon van Zyl, transforming your data into a consistent JSON output has never been easier. Now, your model won't just chat; it’ll produce reliable results. How are you ensuring accuracy in your AI-driven processes? Share your thoughts and experiences in the comments! 👇 #AI #DataScience #SentimentAnalysis #OpenAI #Productivity #TechInnovation #flowise #ai #nocode

RAG vs LLM: The Secret Sauce for Smarter AI Solutions! Have you ever wondered why some AI solutions feel so accurate and context-aware? The answer lies in RAG (Retrieval-Augmented Generation). Here's how it redefines the AI game and why it's different from traditional LLMs (Large Language Models): What's LLM? ✅ Large Language Models, like ChatGPT, generate responses based on pre-trained data. They're brilliant but limited to their training cutoff and can sometimes produce outdated or irrelevant information. RAG! ✅ RAG combines the power of LLMs with real-time, external retrieval mechanisms. ✅ It retrieves relevant data from external sources. ✅ The LLM augments this data with its natural language capabilities. This means up-to-date, contextually rich answers—every time! Key Differences: 💡 Knowledge Scope: LLM: Static knowledge from pre-training. RAG: Dynamic, fetches real-time info. 💡 Accuracy: LLM: May hallucinate or guess. RAG: Grounded in real data, reducing errors. 💡 Use Cases: LLM: General-purpose Q&A or creative tasks. RAG: Domain-specific solutions like customer support, personalized learning, or medical insights. In short, RAG is the bridge between static AI models and dynamic, real-world relevance. Ready to elevate your AI game? 💡 👉 Share your thoughts or DM to dive deeper into this transformative concept! #AIInnovation #RAGExplained #LLM #ArtificialIntelligence #MachineLearning #FutureOfAI #TechExplained #GenerativeAI #NaturalLanguageProcessing #DataDrivenAI #AITech #SmartSolutions #RetrievalAugmentedGeneration #LLMvsRAG #AITrends #AIForBusiness #TechInsights

🤖 Demystifying AI Agents in a Few Simple Steps! 🤖 The spotlight on AI Agents is undeniable, especially in the latter part of the year, if not already. While Large Language Models (LLMs) are impressive, their utility extends beyond mere conversation or paraphrasing. They truly shine when they can take actionable steps and automate processes. However, several challenges hinder their seamless integration into real-world applications: 🤷♂️ Non-deterministic Nature of LLMs: For instance, rephrasing a user's request can lead to various action plans, influencing the final response. ✔️ Accuracy: Certain use cases demand an agent's accuracy to exceed 99% before unlocking their full potential. 📍 Execution: LLMs still grapple with executing tasks efficiently. 🤯 Short-term Memory Limitation: This constraint restricts the number of steps an agent can execute. Despite these challenges, we witness the deployment of agents across diverse industries and applications. Let's delve into an example to understand their functioning: 🤔 User Query: "How many orders did we complete successfully yesterday in City A?" 1️⃣ Planning: The agent strategizes how to proceed with the request. 2️⃣ Action: It identifies the relevant tables. 3️⃣ Observation: It determines the pertinent tables to utilize. 4️⃣ Thought: The agent queries the table schema to identify relevant columns. 5️⃣ Action: It identifies the relevant columns. 6️⃣ Observation: It confirms the pertinent columns. 7️⃣ Action: The agent generates an SQL query. 8️⃣ Tool: It executes the SQL tool with the generated query. 9️⃣ Observation: It retrieves the number of orders in City A. 🔟 Agent Answer In essence, this mirrors the process of learning coding—breaking down tasks into manageable steps. #Agents #AI #GeneralAI #HamzaAliKhalid #MoonSys