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LLMs usually improve through fine tuning and retraining techniques like LoRA. Perhaps prompt optimisation can make them better. Or maybe more sharp evaluation methods like “LLM as a Judge” or “Hallucination Quantification”. But a new paper, “Agentic Context Engineering (ACE): Evolving Contexts for Self-Improving Language Models,” introduces a smarter approach. Instead of updating model weights, ACE evolves the model’s context. Its just like providing a robot smarter and optimized instructions/resources with past experiences instead of opening and reconfiguring its brain. Just like we humans do! Try, learn, repeat! ACE treats context like a growing playbook that records what works and what fails. It learns through three parts. The Generator tries to solve tasks, the Reflector studies what went right or wrong, and the Curator saves the useful lessons for future use. Over time, this loop builds a rich knowledge base that guides future reasoning. Let’s say a coding agent fails to merge CSV files because it didn’t include file paths. The Reflector notes from the terminal output, “Always join directory path using os.path.join().” The Curator stores this as a playbook entry. The next time, the agent reads the note and succeeds immediately. Later, it adds another lesson it learnt during the success e.g. “Use pandas.concat for merging CSVs.” The playbook evolves with each round. But if the Reflector is weak, the loop breaks. Suppose the model fails an API call due to missing authentication. A smart Reflector adds, “Always include access token.” A poor one adds, “Check for typos” or “APIs fail sometimes.” The playbook fills with noise, and the model stops improving. The paper calls this a “polluted context.” In tests, ACE improved performance on reasoning and agent benchmarks by over 10% while cutting adaptation costs by more than 80%. It even matched top commercial agents using smaller models. ACE reframes how models learn. Instead of compressing knowledge into short prompts or retraining from scratch, it builds an evolving, interpretable memory, a living notebook that makes AI systems faster, cheaper, and genuinely self-improving. 🔗: https://lnkd.in/dmVkqdwy #AI #MachineLearning #ArtificialIntelligence #LLM #Research #DeepLearning #PromptEngineering #ContextEngineering #SelfImprovingAI #AutonomousAgents #AIAgents #NaturalLanguageProcessing #MLResearch #AIInnovation #AIProductivity #LangChain #AIApplications #AIEthics #MLOps #AIEvolution

→ Unveiling the Truth About LLM Fine-Tuning: There’s More Than Meets the Eye Most teams dive straight into “fine-tuning” when they want to specialize large language models (LLMs) for their domain. But what if I told you that fine-tuning is just one of three fundamentally different levers you can pull? Each offers unique advantages and trade-offs in control, cost, and engineering complexity. Here’s the full spectrum - from black box to white box. ⚫ Black Box - External Augmentation You keep the model weights untouched but supercharge its abilities via external systems. • Domain Tool Augmentation: LLMs call domain-specific APIs or live inside domain tools for reasoning. • Domain Knowledge Augmentation: Inject explicit structured knowledge or leverage RAG pipelines with unstructured corpora. • Best for rapid prototyping - adapt your domain minus heavy compute costs. ⚪ Grey Box - Prompt Crafting You don’t change the model but craft the inputs smartly. • Continuous Prompts: Dynamically adjusted inputs tailored to tasks - powerful in pipelines. • Discrete Prompts: Static templates for zero-shot or few-shot learning scenarios. • Ideal for low-data, cost-conscious adaptation - where skillful prompt design wins. ⚪ White Box - Model Fine-tuning You get under the hood and update model weights directly. • Instruction-based Fine-tuning: Train your model to reliably follow domain-specific instructions. • Partial Knowledge Update: Inject domain facts without full retraining. • Adapter-based Fine-tuning: Use efficient techniques like Neural Adapters or LoRA for specialization. • Most data- and compute-heavy, yet offers ultimate control and reliability for production. Understanding these three approaches unlocks smarter decision-making. Choose wisely based on your domain, budget, and timeline. → Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2 → Dive into top models like GPT-4o, Llama, and more in one place, Sign up here : https://thealpha.dev follow Vishnu N C for more insights

These 6 repos, 5 courses, and 4 books on AI Agents are all you need to go from 0 → Production Level After exploring dozens of resources on AI Agent development, these stand out as the most structured and practical ones 👇 𝗟𝗲𝘃𝗲𝗹 𝟭 : 𝗚𝗲𝗻𝗔𝗜 & 𝗟𝗟𝗠𝘀 𝗕𝗮𝘀𝗶𝗰𝘀 → Generative AI for Everyone [Course] - https://lnkd.in/eK3ApY_s → Large Language Models (LLMs) – Level 1 [Course] - https://lnkd.in/eVgYg_Sn → Hands-On LLMs [Book] - https://lnkd.in/eFR7xhhR → LLM Course [Repo] - https://lnkd.in/eg9JzpF8 → Prompt Engineering Guide [Repo] - https://lnkd.in/eeftKQGd 𝗟𝗲𝘃𝗲𝗹 𝟮 : 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 → Introduction to RAG [Course] - https://lnkd.in/ebRTW3C3 → RAG Techniques [Repo] - https://lnkd.in/e9YbJN8Q → GenAI Agents [Repo] - https://lnkd.in/euAyXsnQ → AI Agents in Practice [Book] - https://lnkd.in/euAyXsnQ → Agentic Design Patterns [Book] - https://lnkd.in/e6uQm2i2 𝗟𝗲𝘃𝗲𝗹 𝟯 : 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 + 𝗠𝘂𝗹𝘁𝗶 𝗔𝗴𝗲𝗻𝘁𝘀 → Agents towards production [Repo] - https://lnkd.in/eW5A5GrT → LLMs in enterprise [Book] - https://lnkd.in/epmXKaTa → Multi AI Agent Systems with crewAI [Course] - https://lnkd.in/dTudrD55 📚 Whether you’re a data scientist, engineer, or enthusiast — this roadmap gives you a clear path from prompt tinkering → production-grade AI systems. 📌 I regularly share high-signal resources & breakdowns on AI Agents, LLMs, and GenAI tools and core Machine & Deep Learning 👉 Follow to stay ahead of the curve.

AFTER THE DATA COLLECTION AND PREPARATION IN RAG SYSTEM, we start with Embedding Generation in RAG Systems Once the data is collected and prepared, the next key step is Embedding Generation — the process that helps your LLM understand and retrieve relevant information effectively. Here’s what happens behind the scenes ⚙️ What Are Embeddings? Embeddings are numeric representations of text. They convert words, sentences, or chunks into vectors — numerical forms that capture semantic meaning. This allows the system to find “similar” meanings, not just exact matches. ⸻ Embedding Generation Workflow 1️⃣ Choose an Embedding Model • text-embedding-3-small / large (OpenAI) • all-MiniLM-L6-v2 (Sentence Transformers) • Gemini / Cohere embeddings • The choice depends on accuracy vs cost trade-off. 2️⃣ Generate Embeddings for Each Chunk • Pass each prepared text chunk to the embedding model. • The model returns a high-dimensional vector (e.g., 768-dim or 1536-dim). 3️⃣ Store Vectors in a Vector Database • Store them in tools like FAISS, Chroma, Pinecone, or Weaviate. • Each vector is stored along with its original chunk and metadata (e.g., source, page, topic). 4️⃣ Retrieval During Query • When a user asks a question, the query is embedded using the same model. • The system finds the most similar vectors using cosine similarity or dot product. • The retrieved chunks are then passed to the LLM for contextual generation. Keep learning fellas😄❤️ #RAG #LLM #Embeddings #LangChain #VectorDB #AI #MachineLearning #DataEngineering

Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: Credits to Chris Donnelly , make sure to follow! _______ Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: This is my adapted model from a popular framework. I just added a couple steps to squeeze the most out of it. Ultimately, prompt engineering is a vital skill in 2025. But a lot of people don't know where to start. The easiest way to start is by copying a framework ! This one almost acts as a $500/hr consultant... It's saved me so much time since GPT-5 released. 💾 Save this post to use the prompt later: 1. Role 👤 ↳ Tell the AI who it should be exactly. ↳ You get answers grounded in experience. 2. Task 📕 ↳ Set the exact job you want it to complete. ↳ You get a more focused output as a result. 3. Format 📄 ↳ Force structure: i.e. roadmap, KPIs, owner, etc. ↳ The output is like a playbook, not a brain dump. 4. Warnings ⚠️ ↳ Call out the no-no's, ask for zero vagueness. ↳ Forces the AI to stay on brief with its answers. 5. Reasoning 🧠 ↳ Make it justify every move - why, how, and what. ↳ Turns tasks into testable steps and expected impact. 6. Stop Conditions 🛑 ↳ Tell it when to quit: i.e. 90 days, 5 actions, etc. ↳ You avoid the GPT waffling on with useless info. 7. Context Dump 🗑️ ↳ Load it with specifics: i.e. product, target, budget, etc. ↳ Cuts out generic answers and forces relevance. My team and I now use this prompt framework at the start of every new project. Prompting doesn't have to be as complex as it seems. Just have you vision in mind and follow this structure. This works especially well for GPT-5. But the same thinking applies to all LLMs: The clearer you are, the better your results will be. Will you give this prompt framework a go? Let me know how you get on in the comments ⬇️ If you want to learn more about maximising usage of AI... I talk about it often in my weekly newsletter, Step by Step. _______ 👉 Want free PDFs of top LinkedIn infographics? 👇 Get them here: https://lnkd.in/gytiznfT (These are not from this post/creator) Follow SourceSolve for more daily content!

Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: Credits to Chris Donnelly , make sure to follow! _______ Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: This is my adapted model from a popular framework. I just added a couple steps to squeeze the most out of it. Ultimately, prompt engineering is a vital skill in 2025. But a lot of people don't know where to start. The easiest way to start is by copying a framework ! This one almost acts as a $500/hr consultant... It's saved me so much time since GPT-5 released. 💾 Save this post to use the prompt later: 1. Role 👤 ↳ Tell the AI who it should be exactly. ↳ You get answers grounded in experience. 2. Task 📕 ↳ Set the exact job you want it to complete. ↳ You get a more focused output as a result. 3. Format 📄 ↳ Force structure: i.e. roadmap, KPIs, owner, etc. ↳ The output is like a playbook, not a brain dump. 4. Warnings ⚠️ ↳ Call out the no-no's, ask for zero vagueness. ↳ Forces the AI to stay on brief with its answers. 5. Reasoning 🧠 ↳ Make it justify every move - why, how, and what. ↳ Turns tasks into testable steps and expected impact. 6. Stop Conditions 🛑 ↳ Tell it when to quit: i.e. 90 days, 5 actions, etc. ↳ You avoid the GPT waffling on with useless info. 7. Context Dump 🗑️ ↳ Load it with specifics: i.e. product, target, budget, etc. ↳ Cuts out generic answers and forces relevance. My team and I now use this prompt framework at the start of every new project. Prompting doesn't have to be as complex as it seems. Just have you vision in mind and follow this structure. This works especially well for GPT-5. But the same thinking applies to all LLMs: The clearer you are, the better your results will be. Will you give this prompt framework a go? Let me know how you get on in the comments ⬇️ If you want to learn more about maximising usage of AI... I talk about it often in my weekly newsletter, Step by Step. _______ 👉 Want free PDFs of top LinkedIn infographics? 👇 Get them here: https://lnkd.in/gytiznfT (These are not from this post/creator) Follow Proven Leaders for more!

Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: Credits to Chris Donnelly , make sure to follow! _______ Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: This is my adapted model from a popular framework. I just added a couple steps to squeeze the most out of it. Ultimately, prompt engineering is a vital skill in 2025. But a lot of people don't know where to start. The easiest way to start is by copying a framework ! This one almost acts as a $500/hr consultant... It's saved me so much time since GPT-5 released. 💾 Save this post to use the prompt later: 1. Role 👤 ↳ Tell the AI who it should be exactly. ↳ You get answers grounded in experience. 2. Task 📕 ↳ Set the exact job you want it to complete. ↳ You get a more focused output as a result. 3. Format 📄 ↳ Force structure: i.e. roadmap, KPIs, owner, etc. ↳ The output is like a playbook, not a brain dump. 4. Warnings ⚠️ ↳ Call out the no-no's, ask for zero vagueness. ↳ Forces the AI to stay on brief with its answers. 5. Reasoning 🧠 ↳ Make it justify every move - why, how, and what. ↳ Turns tasks into testable steps and expected impact. 6. Stop Conditions 🛑 ↳ Tell it when to quit: i.e. 90 days, 5 actions, etc. ↳ You avoid the GPT waffling on with useless info. 7. Context Dump 🗑️ ↳ Load it with specifics: i.e. product, target, budget, etc. ↳ Cuts out generic answers and forces relevance. My team and I now use this prompt framework at the start of every new project. Prompting doesn't have to be as complex as it seems. Just have you vision in mind and follow this structure. This works especially well for GPT-5. But the same thinking applies to all LLMs: The clearer you are, the better your results will be. Will you give this prompt framework a go? Let me know how you get on in the comments ⬇️ If you want to learn more about maximising usage of AI... I talk about it often in my weekly newsletter, Step by Step. _______ 👉 Want free PDFs of top LinkedIn infographics? 👇 Get them here: https://lnkd.in/gytiznfT (These are not from this post/creator) Follow The Emotionally Intelligent Leader for more!

Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: Credits to Chris Donnelly , make sure to follow! _______ Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: This is my adapted model from a popular framework. I just added a couple steps to squeeze the most out of it. Ultimately, prompt engineering is a vital skill in 2025. But a lot of people don't know where to start. The easiest way to start is by copying a framework ! This one almost acts as a $500/hr consultant... It's saved me so much time since GPT-5 released. 💾 Save this post to use the prompt later: 1. Role 👤 ↳ Tell the AI who it should be exactly. ↳ You get answers grounded in experience. 2. Task 📕 ↳ Set the exact job you want it to complete. ↳ You get a more focused output as a result. 3. Format 📄 ↳ Force structure: i.e. roadmap, KPIs, owner, etc. ↳ The output is like a playbook, not a brain dump. 4. Warnings ⚠️ ↳ Call out the no-no's, ask for zero vagueness. ↳ Forces the AI to stay on brief with its answers. 5. Reasoning 🧠 ↳ Make it justify every move - why, how, and what. ↳ Turns tasks into testable steps and expected impact. 6. Stop Conditions 🛑 ↳ Tell it when to quit: i.e. 90 days, 5 actions, etc. ↳ You avoid the GPT waffling on with useless info. 7. Context Dump 🗑️ ↳ Load it with specifics: i.e. product, target, budget, etc. ↳ Cuts out generic answers and forces relevance. My team and I now use this prompt framework at the start of every new project. Prompting doesn't have to be as complex as it seems. Just have you vision in mind and follow this structure. This works especially well for GPT-5. But the same thinking applies to all LLMs: The clearer you are, the better your results will be. Will you give this prompt framework a go? Let me know how you get on in the comments ⬇️ If you want to learn more about maximising usage of AI... I talk about it often in my weekly newsletter, Step by Step. _______ 👉 Want free PDFs of top LinkedIn infographics? 👇 Get them here: https://lnkd.in/gytiznfT (These are not from this post/creator) Follow JM Leadership Lab for more!

Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: Credits to Chris Donnelly, make sure to follow! _______ Most people are still prompting GPT-5 like it's GPT-4. This is the structure to unlock its full potential: This is my adapted model from a popular framework. I just added a couple steps to squeeze the most out of it. Ultimately, prompt engineering is a vital skill in 2025. But a lot of people don't know where to start. The easiest way to start is by copying a framework ! This one almost acts as a $500/hr consultant... It's saved me so much time since GPT-5 released. 💾 Save this post to use the prompt later: 1. Role 👤 ↳ Tell the AI who it should be exactly. ↳ You get answers grounded in experience. 2. Task 📕 ↳ Set the exact job you want it to complete. ↳ You get a more focused output as a result. 3. Format 📄 ↳ Force structure: i.e. roadmap, KPIs, owner, etc. ↳ The output is like a playbook, not a brain dump. 4. Warnings ⚠️ ↳ Call out the no-no's, ask for zero vagueness. ↳ Forces the AI to stay on brief with its answers. 5. Reasoning 🧠 ↳ Make it justify every move - why, how, and what. ↳ Turns tasks into testable steps and expected impact. 6. Stop Conditions 🛑 ↳ Tell it when to quit: i.e. 90 days, 5 actions, etc. ↳ You avoid the GPT waffling on with useless info. 7. Context Dump 🗑️ ↳ Load it with specifics: i.e. product, target, budget, etc. ↳ Cuts out generic answers and forces relevance. My team and I now use this prompt framework at the start of every new project. Prompting doesn't have to be as complex as it seems. Just have you vision in mind and follow this structure. This works especially well for GPT-5. But the same thinking applies to all LLMs: The clearer you are, the better your results will be. Will you give this prompt framework a go? Let me know how you get on in the comments ⬇️ If you want to learn more about maximising usage of AI... I talk about it often in my weekly newsletter, Step by Step. _______ 👉 Want free PDFs of top LinkedIn infographics? 👇 Get them here: https://lnkd.in/gytiznfT (These are not from this post/creator) Follow The Strategic Leader for more daily content!

🧠 Gradient Boosting Trees vs LLMs: Two Paths to Intelligence In modern AI, two very different approaches are shaping how we solve problems: supervised learning with gradient boosting tree models and Large Language Models (LLMs). Both are powerful, but they operate on different assumptions, data types, and goals. A gradient boosting model, like LightGBM or XGBoost, learns by fitting patterns from structured, labeled data. Every training row has a clear outcome — fraud or not, churn or retain, approved or denied — and the model optimizes directly for predictive accuracy. Its power lies in precision, interpretability, and efficiency. You can track which features drive decisions, explain outcomes with SHAP values, and retrain easily when data changes. In production, these models are fast, stable, and cost-effective. LLMs, on the other hand, learn from vast amounts of unstructured data — text, code, and documents — using self-supervised learning. They’re not trained on a single labeled dataset but on patterns in language itself. Once fine-tuned or prompted correctly, they can reason across multiple contexts: summarizing, generating, classifying, and retrieving information in natural language. Their strength is generalization and adaptability, but they require far more data, compute, and careful alignment to behave reliably. In practical terms, gradient boosting excels when the problem is well-defined and data is structured — fraud detection, credit scoring, pricing, or recommendation ranking. LLMs shine when the data is unstructured or the task requires reasoning and language understanding — summarizing claims, extracting features from documents, or generating insights from text. Rather than viewing them as competitors, the most powerful systems combine them. An LLM can extract or enrich structured features from messy text, and a gradient boosting model can take those features and make precise, explainable predictions. Together, they bridge the gap between human language and statistical precision — forming the foundation of hybrid AI pipelines that are now defining the future of applied machine learning. 👉 Where do you see greater ROI today — in improving classic supervised models or in integrating LLMs to handle unstructured data? #MachineLearning #LLMs #GradientBoosting #LightGBM #AILeadership #MLOps #DataScience #HybridAI