Boosting LLM Performance Using Local Data Layers

In the world of Generative AI, 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) is a game-changer. By combining the capabilities of LLMs with domain-specific knowledge retrieval, RAG enables smarter, more relevant AI-driven solutions. But to truly leverage its potential, we must follow some essential 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Define your problem statement. Whether it’s building intelligent chatbots, document summarization, or customer support systems, clarity on the goal ensures efficient implementation. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 - Ensure your knowledge base is 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲. - Use vector embeddings (e.g., pgvector in PostgreSQL) to represent your data for efficient similarity search. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 - Use hybrid search techniques (semantic + keyword search) for better precision. - Tools like 𝗽𝗴𝗔𝗜, 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, or 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 can enhance retrieval speed and accuracy. 4️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) - If your use case demands it, fine-tune the LLM on your domain-specific data for improved contextual understanding. 5️⃣ 𝗘𝗻𝘀𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Architect your solution to scale. Use caching, indexing, and distributed architectures to handle growing data and user demands. 6️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Continuously monitor performance using metrics like retrieval accuracy, response time, and user satisfaction. - Incorporate feedback loops to refine your knowledge base and model performance. 7️⃣ 𝗦𝘁𝗮𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 - Handle sensitive data responsibly with encryption and access controls. - Ensure compliance with industry standards (e.g., GDPR, HIPAA). With the right practices, you can unlock its full potential to build powerful, domain-specific AI applications. What are your top tips or challenges?

Can we finetune our LLM and retriever together to improve RAG performance? This paper proposes a technique to do exactly that! RAG Basics: When you prompt an LLM, RAG supplies relevant documents. A separate retrieval model computes the probability of each text chunk being relevant and provides the top chunks to the LLM. The LLM generates tokens based on the chunks, prompt, and previous tokens. In Short: Fine-tuning LLMs and retrieval models together improves performance without extensive data processing, enabling better retrieval-augmented generation. LLMs aren't exposed to retrieval-augmented inputs during pretraining, limiting their ability to use retrieved text effectively. Fine-tuning the LLM and retrieval model together can improve performance without requiring extensive data processing. How it Works: Authors from Meta fine-tuned Llama 2 (65B parameters) and DRAGON+, a retriever, to create RA-DIT 65B. They fine-tuned Llama 2 on prompts with retrieved text and questions, and fine-tuned DRAGON+ to retrieve more relevant chunks. Fine-tuning was supervised for tasks like question-answering and self-supervised for text chunk completion. Results: RA-DIT 65B achieved 49.1% accuracy on average across four question datasets, outperforming LLaMA 2 65B with DRAGON+ (45.1%) and LLaMA 2 65B alone (32.9%). With five example inputs, RA-DIT 65B reached 51.8% accuracy. RA-DIT offers an efficient way to enhance LLM performance with RAG, making it a valuable technique for developers. Details: RA-DIT fine-tunes Llama 2 and DRAGON+ to work together effectively, leveraging the strengths of both models to generate better output. By fine-tuning the LLM to better use retrieved knowledge and the retrieval model to select more relevant text, RA-DIT achieves improved performance without requiring extensive data processing. https://lnkd.in/gf4fGVkC

Are you looking for a nice mini AI project? Then build your own RAG system locally! Last quarter, I constructed a fully local RAG system using basic components - all on my computer - without needing any cloud services! The complete code and guide available :) Let’s break down each component: 1️⃣ Streamlit 𝗔𝗽𝗽 - The user interface for uploading documents, managing them, and interacting with the system. Simple and intuitive, it’s the gateway for users to input queries and receive answers. 2️⃣ 𝗢𝗖𝗥 (𝗢𝗽𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻) - Utilizes PyTesseract to convert PDF documents into searchable text. This step is crucial for digitizing printed or handwritten documents into a format that can be processed. Not necessary if you’re uploading data other than PDFs. 3️⃣ 𝗥𝗔𝗚 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - This pipeline cleans the extracted text, chunks it for manageability, enriches it with entity extraction, and generates embeddings for each chunk. It transforms raw text into structured data ready for retrieval. 4️⃣ 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 - OpenSearch Project - Stores and manages the text and text embeddings in a scalable way, allowing for efficient retrieval based on vector similarity and traditional search techniques. 5️⃣ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 - Combines #BM25 (a traditional search algorithm) with #semanticsearch capabilities, ensuring that users can retrieve the most relevant document chunks based on their queries. 6️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 - Structures the user input and chat history to form a context-aware prompt for the LLM, guiding the model to generate precise and relevant responses. 7️⃣ 𝗟𝗟𝗠 𝘄𝗶𝘁𝗵 Ollama - The brain behind the answers. This local LLM processes the prompts based on the retrieved document chunks and delivers concise, contextually appropriate information. 🔄 Flexibility to Swap and Experiment: - We can swap out the #LLM as new open-source models become available. - Change the #OCR method or even remove it completely for image-based data. - Try out different embedding models from Hugging Face — all locally, with full control over privacy. 📄 Curious about how these components work together? Check out the blog post! [Links in the comments] 👇 This system is not only a great way to understand and leverage AI locally but also showcases how scalable these solutions can be. Start locally to experiment and understand; move to the cloud to scale when you’re ready. I hope this helps! Please reach out to me if you get stuck while building the project yourself 😊 —————————————————— Hi there 👋, I am Shantanu, ML lead with 9 years of experience in Data Science, Machine Learning and MLOps. Follow me for more day to day insights in #MachineLearning, #MLOps, #LLM, #AI, #AILeadership etc.

Stop obsessing over which LLM is better. It does not matter if your architecture is weak. A junior dev optimizes prompts. A senior dev optimizes flow control. If you want to move from "demo" to "production", you need to master these 4 agentic patterns: 𝟭. 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) This is your debugging layer for logic. Standard models fail at complex math or reasoning because they predict the answer token immediately. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Do not just ask for the result. In your System Prompt, explicitly instruct the model to "think step-by-step" or output its reasoning inside specific XML tags (e.g., <reasoning>...</reasoning>) before the final answer. You can parse and validate the reasoning steps programmatically before showing the final result to the user. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) This is your dynamic context injection. The context window is finite; your data is not. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 ◼️ Ingest: Chunk your documents and store them as vector embeddings (using Pinecone, Milvus, or pgvector). ◼️ Retrieve: On user query, perform a cosine similarity search to find the top-k chunks. ◼️ Inject: Concatenate these chunks into the context string of your prompt before sending the request to the LLM. 𝟯. 𝗥𝗲𝗔𝗰𝘁 (𝗥𝗲𝗮𝘀𝗼𝗻 + 𝗔𝗰𝘁 𝗟𝗼𝗼𝗽) This is how you break out of the text box. It turns the LLM into a controller for your own functions. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 You need a while loop in your code: 1. Call the LLM with a list of defined tools (JSON Schema). 2. Check if the finish_reason is tool_calls. 3. Execute: Run the requested function locally (e.g., fetch_weather(city)). 4. Observe: Append the function's return value to the message history. 5. Loop: Send the history back to the LLM to generate the final natural language response. 𝟰. 𝗥𝗼𝘂𝘁𝗲𝗿 (𝗧𝗵𝗲 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿) This is your switch statement powered by semantic understanding. Using a massive model for every trivial task is inefficient and slow. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Use a lightweight, fast model (like GPT-4o-mini or a local Llama 3 8B) as the entry point. Its only job is to classify the user intent into a category ("Coding", "General Chat", "Database Query"). Based on this classification, your code routes the request to the appropriate specialized prompt or agent. - - - - - - - - - - - - - - - 𖤂 Save this post, you’ll want to revisit it. - - - - - - - - - - - - - - - - I’m Nina. I build with AI and share how it’s done weekly. #aiagents #llm #softwaredevelopment #technology

𝘛𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘓𝘓𝘔𝘴 𝘧𝘰𝘳 𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘪𝘻𝘦𝘥 𝘥𝘰𝘮𝘢𝘪𝘯𝘴 𝘰𝘧𝘵𝘦𝘯 𝘮𝘦𝘢𝘯𝘴 𝘢 𝘱𝘢𝘪𝘯𝘧𝘶𝘭 𝘤𝘩𝘰𝘪𝘤𝘦: 𝘤𝘰𝘴𝘵𝘭𝘺, 𝘧𝘶𝘭𝘭-𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨 𝘵𝘩𝘢𝘵 𝘳𝘪𝘴𝘬𝘴 "𝘤𝘢𝘵𝘢𝘴𝘵𝘳𝘰𝘱𝘩𝘪𝘤 𝘧𝘰𝘳𝘨𝘦𝘵𝘵𝘪𝘯𝘨," 𝘰𝘳 𝘴𝘭𝘰𝘸, 𝘪𝘯𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘙𝘈𝘎). 𝘞𝘩𝘢𝘵 𝘪𝘧 𝘵𝘩𝘦𝘳𝘦'𝘴 𝘢 𝘵𝘩𝘪𝘳𝘥 𝘸𝘢𝘺? New research introduces a "plug-and-play" memory that gives LLMs domain expertise on the fly, without the usual trade-offs. This is crucial because efficiently adapting models for specialized fields like medicine, finance, and law is one of the biggest hurdles to deploying truly expert AI. A new paper, "𝐌𝐞𝐦𝐨𝐫𝐲 𝐃𝐞𝐜𝐨𝐝𝐞𝐫: 𝐀 𝐏𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐞𝐝, 𝐏𝐥𝐮𝐠-𝐚𝐧𝐝-𝐏𝐥𝐚𝐲 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬," tackles this problem. 𝘛𝘩𝘦 𝘗𝘳𝘰𝘣𝘭𝘦𝘮: 𝘊𝘶𝘳𝘳𝘦𝘯𝘵 𝘥𝘰𝘮𝘢𝘪𝘯 𝘢𝘥𝘢𝘱𝘵𝘢𝘵𝘪𝘰𝘯 𝘮𝘦𝘵𝘩𝘰𝘥𝘴 𝘢𝘳𝘦 𝘦𝘪𝘵𝘩𝘦𝘳 𝘵𝘰𝘰 𝘦𝘹𝘱𝘦𝘯𝘴𝘪𝘷𝘦 (𝘋𝘈𝘗𝘛) 𝘰𝘳 𝘵𝘰𝘰 𝘴𝘭𝘰𝘸 𝘢𝘵 𝘪𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘵𝘪𝘮𝘦 (𝘙𝘈𝘎). 𝘛𝘩𝘦 𝘔𝘦𝘵𝘩𝘰𝘥𝘰𝘭𝘰𝘨𝘺: Instead of costly retraining or slow external lookups, the researchers pre-train a small, separate transformer decoder, the Memory Decoder. This compact module learns to imitate a non-parametric retriever, effectively encoding domain knowledge into its own parameters. It can then be seamlessly integrated with any frozen LLM that shares its tokenizer. 𝘛𝘩𝘦 𝘍𝘪𝘯𝘥𝘪𝘯𝘨𝘴: The results are impressive. A single 0.5B parameter Memory Decoder consistently boosts models ranging from 0.5B to 72B, reducing perplexity by an average of 6.17 points across domains. It achieves this with minimal impact on inference latency, a massive improvement over traditional RAG. The implications are significant. This could democratize domain specialization, allowing for the rapid creation of expert models without massive computational budgets. It paves the way for a more modular AI paradigm, where specialized knowledge can be "plugged in" as needed, rather than being baked into a monolithic model. #AI #MachineLearning #LLM #DomainAdaptation #DeepLearning

Nearly 100,000 people bookmarked Karpathy’s LLM Wiki idea. Almost no one is asking what actually feeds it. Agents should not start from zero every time. Searches. Sources. Research tasks. Answers. All of it should compound into a knowledge base the agent can reuse. That idea is powerful. But after building this pattern into our own Web Search Skills, I think there is one layer people are underestimating: What feeds the wiki? A memory layer is only useful if the data going into it is fresh, structured, and sourced enough for the agent to use again. Otherwise, the agent just becomes very good at remembering stale context. Each search can become part of the agent’s working memory: structured, sourced, and reusable. This is how AI systems start building their own working knowledge over time. Karpathy’s wiki layer is the right mental model. But every memory layer needs a data layer behind it. 𝗣.𝗦. We open-sourced Nimble's Web Search Skills for anyone experimenting with Karpathy-style LLM Wikis. They connect live web search with structured wiki-style memory. Link in the comments. ���� Image credit: Shann³ #AIAgents #AISearch #AIInfrastructure

Bigger context windows will not save your LLM app. Most teams think the solution is to stuff more data into the model. It is not. The real advantage comes from Context Engineering. This is the skill of designing an AI system that feeds the model the right information at the right time. Not by changing the model, but by connecting it to the outside world: • retrieving fresh data • grounding answers in facts • using tools and memory to stay accurate The goal is not to overload a prompt. It is to make the model smarter about what stays active and what gets offloaded. This is what separates basic LLM Q and A from real production systems. To do this right, you need six components working together 👇 ⸻ 1. Agents 🤖 The decision makers. Agents evaluate what they know, decide what they need, choose the right tools, and recover when things go wrong. ⸻ 2. Query Augmentation 🔎 Turning messy user input into precise intent. If the system does not know exactly what the user is asking, everything downstream fails. ⸻ 3. Retrieval 📚 The bridge from the model to your real data. This is chunking, indexing, and fetching the right facts with the right balance of precision and context. ⸻ 4. Prompting Techniques 🧭 Guiding the model with clear reasoning instructions. Chain of Thought, Few shot examples, ReAct style prompting, and more. ⸻ 5. Memory 🧠 Short term and long term. Your app needs to remember past interactions and keep persistent knowledge available when needed. ⸻ 6. Tools 🔧 The action layer. APIs, code execution, web browsing, database calls. This is how your system moves from answering questions to actually performing work. ⸻ This is far more advanced than classic RAG. This is how production systems maintain coherence, access live data, reduce hallucinations, and actually get work done. If you want more breakdowns like this on LLM architecture, RAG systems, and AI engineering, follow my profile here on LinkedIn.

I’ve been using Ollama to run local LLMs for some of my agentic workflows, primarily to keep costs under control. Like many others, I relied on a RAG pipeline to build a knowledge base over internal documents and improve context quality. While it helped, the results were often inconsistent—good in parts, but not always reliable. Recently, I experimented with Unsloth AI, and the experience was genuinely eye-opening. Fine-tuning local LLMs has become dramatically simpler. With Unsloth and Claude Code, you can now fine-tune models directly from your MacBook with a surprisingly smooth, developer-friendly workflow. No heavy infra, no unnecessary complexity—just clean, focused iteration. What stood out most: with the right dataset, a fine-tuned local model can outperform generic, prompt-engineered GPT-4 style usage for specific, well-scoped domains. This feels like a return to first principles—own your data, shape your model, and get deterministic behavior where it matters. If you’re working with RAG today and feeling its limits, fine-tuning is worth a serious look again. Setup guide here for anyone curious: https://lnkd.in/ek56S2f3 Local models are no longer just about cost savings—they’re becoming a real performance lever.

Most discussions about LLM agents treat memory as a retrieval problem. Store information somewhere, fetch relevant chunks, append them to the prompt, and reasoning improves. As agents move from single tools to planner–executor stacks, debate systems, and teams of specialized agents, the dominant bottleneck is no longer model capability but how semantic state moves through the system. Context becomes a dynamic memory substrate rather than a static prompt. The correction proposed in the paper is that agent memory should be designed like computer architecture, not like a document store. Instead of a single knowledge repository, the system needs an explicit hierarchy and protocols that govern how state is shared and updated across agents. Their suggestions is to start with a three layer memory hierarchy: 1. The I/O layer handles raw inputs and environment signals such as text, images, tool outputs, or network events. 2. Above that sits a cache layer designed for immediate reasoning. This layer holds compressed context, recent trajectories, embeddings, and artifacts like tool call results. 3. The final layer is persistent memory where full histories, vector databases, graph stores, and document collections live. The paper emphasizes that agent performance becomes a data movement problem. If the right information never reaches the cache layer at the right moment, reasoning quality collapses even when the model itself is capable. Two protocol gaps emerge when multiple agents operate on top of that hierarchy. The first is cache sharing. In most systems, each agent recomputes reasoning artifacts independently. The authors argue for a protocol where cached artifacts such as intermediate reasoning traces or embeddings can be reused across agents, analogous to cache-to-cache transfers in multiprocessors. The second is a formal memory access protocol. Even when agents share storage, the rules governing read and write access remain vague. Practical systems need explicit decisions about permissions, granularity, and scope. Can an agent read another agent’s long term memory. Are writes atomic. Is the unit of state a document, a chunk, or a reasoning trace. The deeper issue is consistency. In traditional computing, memory consistency models define which writes are visible to which reads and in what order. Multi agent systems face the same problem, but with semantic artifacts rather than bytes. Multiple agents writing plans, evidence, or tool traces into shared memory create stale reads, conflicting updates, and diverging world models unless visibility and versioning rules are defined. Memory in agent systems should be treated as infrastructure rather than storage. That means designing explicit hierarchies, defining read and write contracts between agents, instrumenting cache layers, and treating consistency rules as first class architecture decisions. Paper https://lnkd.in/e4zXGgRz

After about a year and a half working with LLMs I've seen a few tips on how to turn a commercial LLM into your in-house expert: my six-step playbook is below: 1️⃣ Pick the lightest customization that does the job: • Retrieval-Augmented Generation keeps the base model frozen and pipes in your own documents at run time. • Fine-tuning bakes stable expertise directly into the weights. • Hybrid approaches freeze what rarely changes and retrieve what does. 2️⃣ Obsess over data quality: Clean, permission-cleared text matters more than GPU hours. Redact PII, keep training chunks under two thousand tokens, and label a handful of gold-standard examples for every task. 3️⃣ Choose a training method that matches your budget: Full fine-tune for “mission-critical or bust,” Low-Rank Adaptation (LoRA) when you have one GPU and a deadline, instruction tuning for conversational agents, reinforcement learning if safety and tone need tight control. 4️⃣ Stand up an evaluation pipeline before launch: Automated test suites (DeepEval, RAGAs, MLflow Evaluate) score every new checkpoint for accuracy, relevance, bias, and hallucination. Treat prompts like code: unit-test them nightly. 5️⃣ Build guardrails in, not on: Add content filters, prompt-injection shields, and telemetry hooks that log inputs, outputs, and confidence scores. Compliance teams sleep better when monitoring is automatic. 6️⃣ Iterate in production: Canary releases send five percent of traffic to the new model and compare KPIs. Active-learning loops capture low-confidence answers and route them back into the next training batch. Schedule quarterly refreshes so improvement is routine, not heroic. Key takeaway: start with data and evaluation, layer on the lightest customization path that meets accuracy, and measure everything. Do that, and your “off-the-shelf” LLM will start speaking your organization’s language in record time. What’s your go-to tactic for customizing large language models? Drop it below so we can all learn faster. Thoughts?