From Prompt to Context Engineering: AI Workshop

I'm extremely bullish on Platform Engineering, even more so with AI in the picture. You discover late in a project a few clicks you want to track? If you have good documentation and a well defined code structure to do that, it's a breeze to delegate it to a PR from an AI agent, review the functionality and merge. This applies to many capabilities. These golden paths you've defined for engineers are highways for AI agents. In turn, engineers can focus on deeper and more interesting work.

AI Engineering in 76 Minutes: A Complete Speedrun Course If you want a compact, practical tour through AI engineering, this 76-minute video is like sitting with an experienced engineer who talks you through the essentials, fast. It’s friendly to beginners, but it doesn’t skip the things that actually matter in real projects. The course walks through the whole pipeline, step by step:  - What is AI Engineering? (0:00),  - Foundation Models (1:49),  - Evaluating Models (8:40) - Model Selection (14:50). Then it gets hands-on with  - Prompt Engineering (23:15),  - RAG and Context Construction (30:20), - Agents and Memory Systems (36:56). You’ll also find clear sections on  - Finetuning (43:02),  - Dataset Engineering (52:40),  - Inference Optimization (59:45), - and finally, Architecture and User Feedback (1:09:01). This course is a great intro and a handy refresher. It’s not a substitute for hands-on practice, but it gives you a map so you won’t get lost. It also hints at what’s next in the field, like tighter integration between models and product feedback loops. Watch the full speedrun here: https://lnkd.in/eeCtBiMU

Completed “Prompt Engineering: How to Talk to the AIs” by Xavier Amatriain 🎓 Recently, I completed a course on Generative AI — the world where ChatGPT lives. It was a deep dive into how AI systems learn, think, and create. Here’s what I explored 👇 🧠 Generative AI & LLMs Generative AI creates new content — text, images, music, or video. At its core are Large Language Models (LLMs) trained on massive data to understand and generate human-like language. Famous ones include GPT (OpenAI), LLaMA (Meta), Sparrow (DeepMind), and Bard / LaMDA (Google). Beyond Text: Image, Music & Video Tools like DALL·E 2, Midjourney, and Stable Diffusion turn text into visuals. We even explored text-to-music and text-to-video models redefining creativity. ⚙️ How It Works The Transformer architecture powers these models. Key ideas: Attention Mechanism – focuses on important words. RLHF – improves outputs with human feedback. Diffusion Models – create realistic images. Zero-shot Learning – performs unseen tasks. 🤔 AI Hallucination When AI gives confident but false answers — like saying Edison invented the internet. Learning to detect and minimize this was eye-opening. 💡 Prompt Engineering Basics Every great AI output starts with a great prompt: 1️⃣ Instruction 2️⃣ Question 3️⃣ Input Data 4️⃣ Examples We also learned domain-specific prompting for fields like marketing or healthcare. 🧰 Useful Tools Explored Scale Spellbook, Humanloop, Promptable, and Vellum for testing and improving prompts. 🔁 Advanced Prompting Chain of Thought – ask AI to reason step-by-step. Stochastic Prompts – get creative, varied responses. Use </endprompt/> to clearly end instructions. 💬 My Takeaway AI isn’t replacing creativity — it’s amplifying it. Mastering how to “talk” to AI is becoming a vital digital skill for the future. 📢 If you’re curious about how AI like ChatGPT or DALL·E really works — start experimenting, prompting, and learning today! #ArtificialIntelligence #GenerativeAI #PromptEngineering #LearningJourney #OpenAI #Innovation #AICommunity #LLM #Transformers #ChatGPT #XavierAmatriain Check it out: https://lnkd.in/g6mij59B #generativeai #artificialintelligence #largelanguagemodels #promptengineering

🚀 Reimagining the Future of Software Engineering A fascinating new white paper — “A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm” — proposes a bold direction for the next generation of developer tools. 🧠 Key idea: Move beyond “AI that writes code” to AI that understands and reasons about software. By combining neural learning (deep models) with symbolic reasoning (logic, constraints, structure), the neurosymbolic paradigm promises: More interpretable automation Lower computational cost Smarter, self-adapting engineering assistants Why it matters: This hybrid approach could redefine how we design, debug, and evolve complex systems — bringing explainability and reliability back into AI-driven software development. 📄 Read the paper: arxiv.org/abs/2505.02275 #AI #SoftwareEngineering #Automation #NeurosymbolicAI #MachineLearning #Explainability #DevTools #FutureOfWork

🔰 Prompt Engineering — The Art of Talking to the Brain 🧠 Definition Prompt Engineering structures your input so the AI understands context, tone, and intent. It’s the interface between you and the model’s brain. 🔍 Analogy Talking to AI without prompt engineering is like giving vague directions to a taxi driver — it might take you somewhere, but not where you want. 💡 Real-Time Example Zero-Shot: “Summarize this document.” One-Shot: “Here’s one summary example; now do this.” Few-Shot: “Here are 3 examples; follow this style.” Chain-of-Thought: “First reason step by step, then answer.” 💡 Tips & Tricks Always specify role, goal, style, and format. Use delimiters (""") to isolate context. Provide examples wherever possible. 🧠 Memory Trick 🪄 “RGSF — Role, Goal, Style, Format.” 🏁 Conclusion Prompt Engineering is the language of precision. It doesn’t just ask questions — it programs thought patterns inside the LLM.

🚀 New Release: Integrating Data Fusion & Cognitive Architectures – Volume 1 I’m excited to share my latest book: Integrating Data Fusion and Cognitive Architectures: Volume 1 – Foundations and Mechanisms This book tackles a problem every AI/robotics engineer eventually faces: How do we unify raw sensor data with higher-level reasoning, decision-making, and explanation? For decades, data fusion and cognitive architectures evolved in parallel—each strong on its own, but rarely combined into a single operational intelligence pipeline. This book closes that gap. Inside, I break down: How to connect Bayesian inference, Kalman filtering, control theory, and symbolic cognition into one continuous reasoning system. How modern cognitive architectures (SOAR, ACT-R, LIDA) align with the JDL Data Fusion Model. How perception transitions into decisions that explain why, not just what. Real-world design patterns for building intelligent systems that can adapt, self-correct, and operate autonomously. If you're working in AI, robotics, autonomous systems, defense tech, or cognitive computing, this volume gives you the mathematical and architectural foundations to build machines that reason, anticipate, and learn—far beyond conventional ML pipelines.

I’m sure you’ve heard that the AI industry has moved from obsessing over prompt engineering to now understanding context engineering. In every LLM application, there are different types of context that shape how the model reasons and acts. At the simplest level, context can be: → User context: what the user says or does → System context: what the application provides as background → Memory context: what the model or agent recalls from past interactions When agents interleave LLM calls and tool calls, they don’t just alternate blindly, so they use feedback from tools to dynamically refine the next LLM call. This creates a continuous reasoning loop: predict → act → observe → re-prompt. That’s where context engineering starts to become an art. Broadly, we can think of context engineering as four key operations: → Write context: crafting inputs that guide model behavior → Select context: retrieving or filtering relevant information → Compress context: distilling large knowledge into limited tokens → Isolate context: keeping different reasoning threads independent At the end of the day, context is what gives intelligence its continuity. Models generate responses, but context is what gives them understanding. If you want to learn more about Context Engineering, join our 1.5 hour hands-on workshop on 8th November where Arvind and I will be covering both theory + demo: https://lnkd.in/dvFmMiRr

Context Engineering is an emerging discipline that should be part of any production-grade GenAI application. It brings together elements like prompt engineering, context versioning, observability, and lifecycle management of prompts, agents, and tools. While it’s distinct from AI governance, it plays a critical role in the GenAI software development lifecycle, ensuring that systems are not only powerful but also reliable, traceable, and adaptable.

Last year I built an AI/LLM experiment called LLM Context Steering, where the model has access to multiple RAG data sources, but before loading any RAG data sources, the model attempts to identify context sentiment from the user. Once the context was identified, the application then loads the appropriate RAG data source based on what it believes the user's context to be. This way, you could load different sets of data (via RAG) for the model to use, depending on what the user is talking about. This is how you can provide very granular and context-specific information for a user, across domains that may share terminology or key words (without the model getting mixed up, and crossing context boundaries). But it did not seem as complex as the concept of Context Engineering being described here. In my approach, I just used specialized prompts to instruct the model to generate hidden variable data that the user could not see, and then passing those variables into the application to influence the data loading construct. The result was that the application maintained a context "state" that could be dynamically influenced by the user, as they interact with the LLM.