Welcome back! The RAG loop most teams still run falls apart the moment a real question arrives. Today covers the six-part stack that replaced it, a path through Anthropic's 10 free courses, the machine learning map that no longer fits on one slide, and the difference between deploying a model and building a system.
In today's Generative AI Academy Newsletter:
RAG: Why does embed, top-k, pray stop working?
Courses: Where do you actually start with 10 free ones?
Machine learning: What does the field look like in 2026?
vLLM: Why does your system break at 1,000 users?
Stop shipping 2024 RAG in 2026
The entire RAG ecosystem, updated on one page.
Most teams still run the same loop: embed docs, top-k search, pray.
Then real questions arrive and retrieval falls apart.
The 2026 sheet has 6 moving parts:
→ Query transformations. Rewrite the question before you search it.
→ Routing. Send each query to the right store, tool or MCP server.
→ Indexing. Late chunking, RAPTOR trees and contextual retrieval, so no chunk arrives orphaned.
→ Retrieval. Hybrid BM25 plus vector search, then rerank before a single token gets generated.
→ Generation. Agentic loops that grade their own retrieval. Self-RAG, CRAG.
→ Evals. ragas, Phoenix and DeepEval scoring faithfulness before your users do.
And the 4 additions that define 2026: Agentic RAG, MCP tools, agent memory, multimodal retrieval.
They all point the same direction. The model stopped waiting for context and started going to get it.
Learn AI from the people building it
AI is rewriting how work gets done, and the gap between people who keep up and people who fall behind is widening fast.
GenAI Academy is how you stay on the right side of it. Every course is built and curated by working AI experts, not theorists, so you walk away with moves you can use the same afternoon. Here is what to start with.
Claude Starter Course (free)
The fastest way from "I opened Claude once" to using it well every day. Set up, prompt and get real output in an hour.
Claude Practitioner's Guide to AI Agents (free)
Go past chat. Build agents that take a task and carry it through, with the patterns practitioners actually rely on.
How to Stop Hitting Your Claude Limit (free)
The limit usually hits mid-task on something small. This course shows you why, then hands you 24 fixes across Claude.ai, Cowork and Claude Code so it stops happening.
For finance and operations. Copilot for Excel, Python in Excel and Power Query on the tasks that eat your week, with every session a real job you can run the same day.
Two sessions on building autonomous Governors that mediate between competing industrial KPIs in real time. For anyone whose AI pilot hit its target and broke three others.
Three free courses to start with. Two advanced tracks when you want to go further.
Anthropic put out 10 courses on how to actually use AI. All free
We went through them so we could tell you where to start, because a list of 10 links is useless without a path.
Here's how we'd use it.
If you're still finding your footing with AI, these three are all you need to start. In order:
1️⃣ Claude 101
The basics of using Claude for everyday work. Setup, core features and where to go next once you're comfortable.
2️⃣ What You Can Do with AI
A tour of what these tools are actually good for, aimed at people who haven't found their use case yet.
3️⃣ AI Capabilities and Limitations
A neutral look at how LLMs work and where they fall over. No hype in either direction.
Don't skip the third one. Most of the AI mistakes I see in companies come from people not knowing what the tool can't do.
If you lead a team and want to see where this is heading, go here next:
4️⃣ Claude Guide to AI Agents
The step past chat. What an agent is, when to reach for one and the patterns practitioners rely on.
5️⃣ Introduction to Cowork
Claude works directly with your files and folders, which is the closest thing to delegating a task rather than asking a question.
6️⃣ Introduction to Model Context Protocol
How Claude connects to your tools and data. MCP is the layer turning a chatbot into something that can act.
7️⃣ AI Fluency: Framework and Foundations
The 4D framework: delegation, description, discernment and diligence. Useful whether you're new or already deep in this.
And if you build, this is where the depth is:
8️⃣ Claude Code 101
Claude in the terminal. Setup, project context and the habits that separate casual use from real output.
9️⃣ Introduction to Subagents
Delegating to specialized agents so your main conversation stays clean and your context stays useful.
🔟 Introduction to Agent Skills
Packaging repeatable work as Skills, sharing them with your team and deploying them across an organization.
Full catalog here.
This is what Machine Learning looks like in 2026
And yes, there are a few updates you should know about ↓
Machine Learning used to fit on one slide: a handful of algorithms, some regression, some classification, done.
Not anymore.
The full map now spans supervised, unsupervised, reinforcement, self-supervised, semi-supervised, and transfer learning, each branching into a dozen techniques of its own.
Deep Learning alone covers CNNs, RNNs, Transformers, and Graph Neural Networks.
Generative AI sits as its own massive branch, covering LLMs, diffusion models, GANs, and multimodal systems.
What used to be a single field is now closer to an entire ecosystem, and keeping up with all of it properly takes more than the classes people learned 5 years ago 🫠
GenAI Academy is built for exactly that pace of change.
One place to go from AI basics through to advanced, practical skills.
Courses like Mastering Claude Code walk through how developers are actually shipping faster: orchestrating agent teams, building knowledge systems that improve over time, running autonomous loops.
The Claude Starter Course gets total beginners properly set up in 35 minutes if you're just getting going.
Enhance Your CV with ChatGPT
Your CV gets ten seconds.
That's how long a hiring manager spends matching you against a job description. They scan for their language, not yours. Miss it and you're skipped, however qualified you are.
So the rejection usually wasn't about your experience.
The fix is rewriting the CV per application, which nobody does across ten roles. ChatGPT closes that gap. Paste in your CV and the job description, and twenty minutes gets you a tailored version, a LinkedIn summary and a clear read on where you're strong.
Run it on a role you already got rejected from, using the CV you actually sent. The difference usually explains the outcome.
If three job descriptions name the same missing skill, that's a pattern worth acting on.
Changing industries? Use it to translate your experience into the new field's vocabulary.
Feed the tailored CV and job description back in for a cover letter draft. It won't be finished, and it beats a blank page.
One caution. Reframing in their language is the point. Inventing experience is a different thing, and it surfaces in the interview.
Your system works at 10 users and breaks at 1,000
That gap is the difference between an LLM and vLLM.
The LLM is the brain. Llama, Mistral, Qwen, Gemma. The model trained to understand and generate language.
vLLM is the engine that serves the brain. An inference layer built to run models efficiently under load.
User → API → vLLM → LLM → Response
Deploy an open-source model and ten users hit it. Everything works. A thousand arrive at once and you get GPU memory pressure, queuing, poor throughput and an infrastructure bill nobody approved.
vLLM handles that with PagedAttention, continuous batching, KV-cache optimization and tighter GPU memory management. More requests, less wasted compute.
Two different questions. LLM engineering asks how capable the model is. Inference engineering asks how efficiently you can serve it. The second only shows up when a prototype becomes a product.
The full stack: model for intelligence, inference engine to run it, infrastructure for GPUs, API layer for your applications, observability for latency, errors and cost.
The common mistake is assuming that deploying an LLM means you built a system.
You deployed a model. The system comes after.
Everything else you shouldn't miss
Meta returns to open-source roots with Glimmer: Muse Glimmer is a small, fully open model that runs agents entirely on-device, launched alongside Zuckerberg's essay on why superintelligence belongs to everyone, with Muse Spark 1.2 weights to follow.
Claude is about to start watermarking everything it writes: The mark hides in the text so software can spot it and people can't, survives copy and paste, and covers Claude everywhere under an EU code that took effect August 2.
Claude Sonnet 5's launch price is now permanent: The $2 per million input and $10 per million output rates were set to expire August 31, and Anthropic has made them the real price.
Zuckerberg wants the industry to rethink the rules on distillation: He calls models learning from other models a core open-source principle, a striking claim four months after Meta stopped giving its own weights away.
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