I’ve been working in AI for close to a decade, starting well before today’s LLM-driven wave.

My work is grounded in understanding AI across layers — from hardware, AI models, and data to system design, reliability, and scale. I approach AI as an end-to-end engineering problem: not just modeling, but how models interact with data, infrastructure, and real-world constraints.

Alongside this, I write The AI Merge, a technical newsletter on AI foundations and systems engineering, focused on clear thinking and practical system design rather than hype.

I’m currently building an AI Systems course, centered on end-to-end system design, for engineers who want a deeper, systems-first understanding of modern AI.

🗞️ Read The AI Merge: https://read.theaimerge.com

Alex Razvant

A lot of courses out there teach AI & LLMs, but very few touch on Inference

Sure, it's easy to grasp the fundamentals of what LLMs are and how to build systems around them, but scaling out an LLM out of a lab of 1-2 H100s running it and a single concurrent user session is a big challenge.

Efficient inference is one of the largest barriers to LLMs in production.

Batching, managing memory, concurrent user sessions, GPU saturation, and sharding with/or MIG (Multi-Instance GPUs), parallelisation are key concepts for engineers to know, understand, and work with or around them.

I've had this list of resources saved for a while, which fills some of those gaps.

1. Good Courses / Videos
- CS336 Stanford (Lecture 5-11): https://buff.ly/9o4jRxl
- GPU MODE: https://buff.ly/bPEHYL0 
- LLM Inference Patterns: https://buff.ly/4ASDS5c
- LLM Inference (NVIDIA): https://buff.ly/muDMcor 

2. Repositories / Blogs
- GPU Performance Engineering (E.Andere): https://buff.ly/sfGJNMK 
- AI Performance Engineering (C.Fregly) https://buff.ly/8ey3GPa 
- Inside NVIDIA GPUs (A.Gordic): https://buff.ly/wmgNwMg 
- Understanding LLM Inference (my article): https://buff.ly/NrNU3Qz 

I don't think there's a clear, step-by-step guide on how to master AI Perf Engineering.
The best bet is to study the core pieces of the models and the hardware they run on.

Then, learn the rest on demand.

undefined undefined

Neural Bits CTA

Join 4100+ engineers and get a weekly newsletter on advanced AI/ML. 👉 https://neuralbits.substack.com/

Kubrick

Ready to start building?

--- Resources ---
📌 Kubrick Code Repository
https://github.com/multi-modal-ai/multimodal-agents-course

📌 Neural Bits Newsletter (mine)
https://multimodalai.substack.com/

📌 The Neural Maze Newsletter (Miguel's)
https://theneuralmaze.substack.com/

Senior AI/ML Engineer | Author of NeuralBits Newsletter – helping you build efficient AI Systems

Hard work always pays off, be consistent!

I'm a Senior Machine Learning Engineer with a keen interest in developing AI/ML solutions for real world problems and helping others get started on their Machine Learning Journey.

Let's connect @:
📘 https://medium.com/@alexandrurazvant
✉️ alexandrurazvant@gmail.com
🌐 https://www.neuraleaps.com

Upskill your AI Engineering!

AI Engineering Hub

Senior AI Engineer | Writing The AI Merge Newsletter | Helping Engineers Build AI Beyond Demos

The 5 structure-layers of any LLM Model.
(Understand these, and you'll understand every OSS model on HF)

With each open-source LLM released, you'll find the model weights on Hugging Face.

When you go to the Files section, you won't find a `model.xyz` file, but multiple files, usually 10-15 that compose the entire LLM.

Here's the blueprint to understand them:

1️⃣ 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝘾𝙤𝙣𝙛𝙞𝙜𝙪𝙧𝙖𝙩𝙞𝙤𝙣 (config.json)
This one contains metadata on model architecture, layer activations, sizes, vocabulary size, number of attention heads, model precision, and more. 

The transformers library knows how to parse this config and build the model architecture. 

2️⃣ 𝙈𝙤𝙙𝙚𝙡 𝙒𝙚𝙞𝙜𝙝𝙩𝙨 (the .safetensors splits)
LLMs have B (billions) of parameters.
For safer download, model weights are split into chunks/shards.
The formats used for weights are either Binary or Safetensors.

Safetensors format is an alternative to the default Pickle serializer that PyTorch (pt) uses, as it’s vulnerable to code injection.

3️⃣ 𝙇𝙖𝙮𝙚𝙧 𝙈𝙖𝙥𝙥𝙞𝙣𝙜 (the *.index.json)
Since the models are large and weights come as part files (e.g., 0001-of-0006, 0002-of-0006, etc.), this file stores a sequential map of the model architecture.

This specifies what layers are part of what model chunk.

4️⃣ 𝙏𝙤𝙠𝙚𝙣𝙞𝙯𝙚𝙧 𝘾𝙤𝙣𝙛𝙞𝙜 (tokenizer.json)
The tokenizer config file contains metadata about the tokenizer used to train this model. 

It contains the class name used to instantiate the tokenizer, the layer names, the structure of how the inputs are processed before passing through the model, and special tokens.

The LLM generally uses 𝘴𝘱𝘦𝘤𝘪𝘢𝘭_𝘵𝘰𝘬𝘦𝘯𝘴 as markers to stop generation.

5️⃣ 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣 𝘾𝙤𝙣𝙛𝙞𝙜 (generation_config.json)
These configuration files contain metadata for Inference, such as Temperature and TopP/TopK thresholds or context window size, with which the model was trained. 

When you load your model using Transformers.from_pretrained(), all these files are processed to set up everything you need to actually use that model.

---
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The 4 patterns of LLM Inference.

Deploying LLMs is not the same as deploying traditional ML models. The differences show up most clearly at inference time. 

Some principles such as optimized models, infrastructure, GPU saturation and usage or distributed systems concepts still apply.

But LLM inference adds a new layer of complexity, as Transformer models inference is way more nuanced.

With LLMs, the output is generative and not a fixed size having batch-size input = batch-size output that more traditional ML or Deep Learning models had.

LLM inference splits into two stages, Prefill and Decode, this creates 4 distinct patterns an AI Engineer/Dev should know about before optimizing or deploying.

⇢ In the prefill phase, the model processes the entire input prompt and computes the key-value (KV) cache. In benchmarks, we'll often see this metric as `pp` or `prompt processing` speed, which is fast.

⇢  In the decode phase, it autoregressively generates tokens one at a time, updating the KV cache at each step. In benchmarks, this is refered to as `tg` or text-generation speeds, and this process is memory-bound as tokens have to travel within the GPU's memory during KV cache updates.

Understanding this gives you these four distinct LLM inference patterns:

SISO -- Short Input, Short Output
→ Low latency with fast generation. If using this pattern, aim to optimize for high concurrency.

LILO --  Long Input, Long Output
→ The most compute-intensive requires distributed inference, ideally with shared KV cache across nodes or tensor parallelism.

LISO -- Long Input, Short Output
→ This one has heavy prefill and quick decode. GPUs with enough VRAM would parallelize the prefill and generate fast.

SILO -- Short Input, Long Output
→ Light prefill, which runs fast but is heavier on decode. Decode latency dominates here.

---

𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The longer the output, the more time is spent in the decode loop.
Before fine-tuning your own LLM and deploying it, also consider the inference pattern your system will target.

Summary:
- In LLM inference, there are 4 patterns.
- For LISO, the generation and TFTT are fast.
- On SISO, everything is fast.
- For LILO,  a lot of computing.
- For SILO, prefill is fast, generation is slow.

---

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If you'll ever try to run LLMs locally - you'll bump into llama.cpp

Most guides tell you what to use for Edge/CPU/low-end GPU inference - but not what’s actually happening under the hood. 

No context on:
- How is the model checkpoint parsed?
- How are tensors unpacked and mapped?
- How do GGML, GGUF, and llama.cpp fit together?

✅ So I went through the codebase myself. 

I spent a few hours digging through llama.cpp, PRs, and community threads, then turned everything into a single sequence diagram that shows the flow end-to-end. 

The key pieces of the stack:
> GGML - the ML Tensor Library and execution primitives (ops, mappings, kv-cache)
> GGUF - the format for storing quantized LLM models and Quantization types.
> llama.cpp - is the C++ inference engine, building on top of GGML & GGUF, wiring everything together.

If you’re running local LLMs, this is the stack you’re relying on.

If you want to understand why things break, how quantized models are actually loaded, this breakdown will help. 

---
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You should read more code.

If you’re already a Software Engineer or AI / ML Engineer, one of the fastest ways to level up right now is to study how big AI teams write and ship - in public. 

Many top companies and major contributors to the AI space have been open-sourcing their internal frameworks, kernels, or infrastructure libraries. 

You might feel overwhelmed by how fast AI is going. But this is a great learning opportunity if you know where to look.

Learning doesn’t just happen from papers and tutorials, especially if you already build software.

You should:
- Read real production code
- Study PRs or commit history on major/popular repos.
- See how teams structure codebases, design APIs, configs, tests

Check what gets reviewed, what gets rejected, and why.
This kind of learning will upgrade your engineering intuition. 

Here are some excellent codebases to learn from.

✅ From DeepSeek:
1. FlashMLA  - A library of high-performance attention kernels.
Link: https://buff.ly/XbFnPA7 

2. DeepEP - A Communication Library for high-throughput MoE workloads.
Link: https://buff.ly/3qxMc3S

3. 3FS - A Distributed Filesystem for AI Workloads, for AI training and inference.
Link: https://buff.ly/ovfW47A

✅ From NVIDIA:
1. Dynamo - A datacenter-scale Inference Serving Framework for GenAI workloads.
Link: https://buff.ly/WyAMzBz 

2. NeMo  -  A Scalable Generative AI Framework, for training and deploying LLMs and VLMs.
Link: https://buff.ly/L9KTuJp 

✅ From vLLM:
1. The vLLM Library  - One of the most popular LLM inference engines, designed for scale.
Link: https://buff.ly/GLCfoIM

✅ From SGLang:
1. The SGLang Engine - A high-performance serving engine for LLMs & multimodal models.
Link: https://buff.ly/m4HhsWQ 

✅ From llama.cpp
1. The llama.cpp Engine - A minimal C/C++ inference engine for LLMs with broad hardware support and quantization options.
Link: https://buff.ly/u9yaXwp

📌 Don't study everything, you won't be able to.

Instead, pay attention to:
⇾ Code structure & abstractions
How are core functionalities separated (core logic vs hardware-specific optimizations)?

⇾  PRs & commit history
What context do authors provide? How do reviewers push back?

⇾  Interfaces & public APIs
How are public APIs designed?

⇾  Tests & benchmarks
How do the test suites look? What CI pipelines do they have, profiling? 

Don’t just use these libraries. 
Read them.

That will build your intuition and help you get better at your craft, either as a curious learner or an engineer as part of a larger team at your job.

---
I distill my learnings with my audience on The AI Merge.
Check it here (8700+ engineers already do that) https://buff.ly/2rwqlNL

I have a surprise for my European audience, for NVIDIA GTC 2026 (March 16–19)

🎁 I’m giving away 1 x NVIDIA DGX Spark to someone who joins me for Virtual NVIDIA GTC 2026. 

I’ve spent the last 6 months building on NVIDIA DGX Spark, and when building AI Systems locally, the Spark is an incredible machine to learn, prototype, fine-tune, build and iterate fast.

Especially if you want more control over your entire stack.

NVIDIA GTC is the premier global AI conference, where developers, and researchers come together to explore the next wave of AI across every industry and application.

✅️ Virtual GTC is free. 

𝗛𝗼𝘄 𝘁𝗼 𝗲𝗻𝘁𝗲𝗿 𝘁𝗵𝗲 𝗗𝗚𝗫 𝗦𝗽𝗮𝗿𝗸 𝗴𝗶𝘃𝗲𝗮𝘄𝗮𝘆:
𝟭. 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 for GTC 2026 using my link: https://buff.ly/aKgT9BA
𝟮. 𝗔𝘁𝘁𝗲𝗻𝗱 at least 1 virtual session (keynote does not count) - virtual sessions are free
𝟯. 𝗙𝗶𝗹𝗹 𝗼𝘂𝘁 𝗺𝘆 𝗴𝗶𝘃𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿𝗺 𝗮𝗳𝘁𝗲𝗿 𝗮𝘁𝘁𝗲𝗻𝗱𝗶𝗻𝗴

I’ll share the giveaway form link as we get closer to GTC 2026. 𝗦𝘁𝗮𝘆 𝘁𝘂𝗻𝗲𝗱.

Here are a few sessions that caught my eye (links below):
1. AI Factories in Europe: Building the Foundations for Scalable Intelligence [S81899] 
2. Accelerate AI Through Open-Source Inference [S81902] 
3. Teach AI to Code in Every Language With NVIDIA NeMo [S82306] 
4. From Data to Meaning: Vision-Language Models Shaping the Cities of Tomorrow [S81867] 
5. Your Learning Pathway: Get Certified for Career Success [C81544] 

If you’re planning to attend, remember to register here: https://buff.ly/aKgT9BA 

Follow me here and stay tuned for the giveaway form.

Underrated advice to grow as an Engineer

1. Go Depth first (2–3 years)
Pick one stack (language + framework + system) and get good at it.

Breadth gets easy after.
I know it's tempting to know a bit out of everything, but you'll spread yourself too thin, too soon.

2. If you're a junior/mid - talk to senior/staff engineers (smartly)

Don't ask “What should I do?” 
Instead, ask: “Here’s what I tried, here’s what happened, what would you check next?”

They've seen enough systems and code to spot patterns early on.

2. Get good at Code Reviews
Express your points, think past the small code component - oversee the entire system.
Don't nitpick for code changes unless necessary. 

3. No Vibe-coding
With each feature/component you build, spend time gathering context, asking for options, plan, review, verify and write the hard paths yourself.

One practice that works for me is, after drafting a plan for my coding Agent (in Plan Mode), I ask if it's got any questions for me, before starting to work.

It usually comes up with great points.

3. Study outside work
That's an easy switch.
You do it already by consuming content, scrolling through posts and watching videos.
But make sure you focus on what actually helps you, not all the news/hype out there.

Work getting better, use AI as leverage.

A better way to manage complex configurations in your AI/ML projects.

AI/ML systems usually have more moving parts and more configuration surfaces (data, training, evaluation, inference, orchestration). Keeping track of which configuration subset was used can get difficult fast. 

Sure, as per Factor III (Config) in 12-factor apps, environment-specific config can be stored as env variables.

However, env vars alone are usually not enough. You might need hierarchical configs, runtime composition, overrides etc.

For instance, on a current project I'm working on, I have:
- 6 MLOps pipelines that build the raw_data > deployed model lineage.
- 2 edge services that ingest video, route frames to Inference Servers
- 3 MCP servers, each with their own connection details, tools and resources
- 2 Agents, wired through A2A - each with their own subset of instructions and MCP

In this setup, during testing, services often require runtime overrides (MLOps pipelines), composition or swap (training/eval), and sometimes they share the same configuration domain. 

When configuration gets more complex, I found Hydra + OmegaConf to be the way to go.

Here's what you need to know:
1. 𝗢𝗺𝗲𝗴𝗮𝗖𝗼𝗻𝗳 is a hierarchical configuration system explicitly designed for complex applications like ML pipelines. 

You can:
↳ Merge multiple configuration files directly through runtime composition.
↳ Access fields via attribute notation "config.model.optimizer" or dict notation config["model"]["optimizer"]
↳ Add Type safety against schemas such as dataclasses or Pydantic base models.

2. 𝗛𝘆𝗱𝗿𝗮 builds on top of OmegaConf, creating a full-featured framework.

You can:
↳ Group multiple YAMLs into a single configuration file and reference them directly by filename.
↳ Run a sweep, same application - multiple configurations in parallel.

Concrete examples:
1. Your hydra config could look like this (config.yaml)
``` config.yaml
defaults:
  - data: mydata
  - training: trainAB
  - inference: inferAB
  - _self_ 
```

When loading the `config.yaml` file using Hydra, we get a nested configuration object, from which we can access any field 
(cfg . inference . batch_size / cfg . training . batch_size)

2. Override any config value at runtime. 
For example, if in the config.yaml, if you have:
```
model:
 optimizer: SGD
```

At runtime, you can override the value with:

```
python train model.optimizer=Adam
```

𝗧𝗵𝗶𝘀 𝗮𝗹𝘀𝗼 𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗻𝗲𝘀𝘁𝗲𝗱 𝗰𝗼𝗻𝗳𝗶𝗴𝘀.
↳ Run multiple configurations simultaneously using Hydra multirun.

```
python train -m data.dataset=A1, B12, C55
```

This will launch multiple runs (one per dataset value) and create separate output directories for each run. 

---

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Qwen3.5 4B and 9B around the corner? 👀

Code is cheap nowadays. Design and planning is not. 

Some, keep measuring output by volume. 

I've been doing AI Assisted Programming for the past 2.5 years, on real codebases.

Online, I keep seeing many claim to push 10k Lines of Code a day to production and not reviewing anything.

I think that’s mostly marketing.
The more code added to a codebase, the larger the surface area.

Software isn't written once. It's designed to evolve.
What works now, should be easily changed or improved in the future.

The question you should ask: 
“Can we ship changes that are consistent and safe to release?”

A few safeguards that actually worked for me:
1. Treat AI code as proposal, not truth. Don’t accept diffs you can’t explain.
2. Put a harness on the agent. (Cursor Rules / AgentsMD / ClaudeMD / Skills / Plugins)
3. Keep Agent Rules, Skills and Plugins small, scoped and enforceable.
4. Add guardrails only after noticing Coding Agent failures.
5. Keep PRs reviewable. Minimal context + a 1-pager attached on the PR for big moves.
6. Reviews should't focus on preference. Skip nitpicks. Focus on the entire workflow.
7. Pre-commit + CI parity. Catch drift before you waste a CI cycle.
8. Write a handoff note to your tomorrow-self so you catch-up faster on the next day.

For an Engineer, AI helps, but only when you stay in control of constraints.

---
I explain complex topics in AI, AI Engineering and building AI Systems.

For free.

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Six Tips on AI Assisted Programming

Collabs: collab.shivanivirdi@gmail.com
Hi, I’m Shivani.
AI Engineer | Specialising in RAG, Context Engineering & Agentic Systems | Founder, NeoSage

I help engineers and founders understand and build production-grade LLM systems that actually work.

What I write about:
- RAG architectures, retrieval, ranking & evaluation
- Context engineering (the layer most teams overlook)
- Agentic AI & tool-using systems
- AI system design, patterns, and reasoning frameworks

🎯 Who I am
Ex-Microsoft/AWS/Adobe engineer building secure distributed systems and user-facing intelligence features. Now full-time in AI engineering education and NeoSage — where I teach the systems behind reliable, scalable LLM applications.

🚀 What I do
I break down advanced AI engineering into actionable mental models, design blueprints, and repeatable frameworks.

If you're building RAG, improving accuracy, or designing agents, my content will make you better.

🌍 Why I do it
Because AI isn’t magic, it’s systems engineering.

And the world needs more people who can bridge the gap between research and actual application.

Let’s build the future of intelligent systems together.

Shivani Virdi

Everyone talks about AI agents. But no one shows the 8-level capability ladder that gets you there. (Most production apps don't need more than Level 4-6.)

Level 1: Generation
↳ LLM takes a prompt, produces output from what it learned in training.
↳ Chatbots, summarizers, classifiers, content generation.
↳ Powerful, but the model only knows what it was trained on.

Level 2: Knowledge Grounding
↳ LLM gets access to external data at query time.
↳ Embeddings convert text to vectors. Vector databases make them searchable. Chunking determines what pieces can be retrieved.
↳ The quality of what you retrieve determines everything downstream.

Level 3: Tool Use
↳ LLM goes from answering questions to doing things.
↳ Call APIs, execute code, query databases, read files, interact with external systems.
↳ Function calling, MCP, structured tool definitions. 

Level 4: State, Memory and Context Engineering
↳ LLMs are stateless. Every call starts from zero.
↳ Memory gives the system continuity. Short-term for conversation. Long-term for user preferences and knowledge across sessions.
↳ Context engineering is the discipline of controlling everything the model sees per call. What documents, what history, what examples, in what order. 

Level 5: Reasoning and Evaluation
↳ The system assesses its own output before responding.
↳ Hallucination detection by checking generated answers against retrieved sources. Confidence scoring. Evaluation pipelines that catch bad answers before users do.

Level 6: Orchestrated Workflows
↳ Developer builds the architecture. Defines what paths exist and what conditions trigger each.
↳ LLM evaluates at each checkpoint and picks which path to take.
↳ Example: retrieve documents, LLM grades their relevance. 
High relevance: generate. 
Low relevance: rewrite the query and retrieve again. 
Irrelevant: fall back to web search.

Level 7: Autonomous Agents
↳ The LLM runs the loop. Developer provides tools, a system prompt, and a step limit.
↳ The LLM looks at the query, picks a tool, observes the result, decides what to do next. It keeps going until it has enough or hits the limit.
↳ A factual question might take two steps. A complex research query might take five across different sources. The path is different every time.

Level 8: Multi-Agent Coordination
↳ Multiple specialized agents, each with their own tools and focus, coordinate on a shared task.
↳ One retrieves, another analyzes, another validates. Like a team, not a single operator.
↳ Most production systems don't need this yet. The ones that do are handling complexity no single agent can.

Most teams jump straight to Level 7 and wonder why it breaks.
The systems that actually work in production nail Levels 2-6 first.
_______
👋 P.S. 50+ engineers from Microsoft, Adobe, Amazon, Shopify and Visa just climbed Levels 1-7 hands-on in 6 weeks. If you want a structured way to build enterprise-grade AI systems, April cohort is open (link in comments).

♻️ Repost to help your network upskill

AI Engineering | Founder @ NeoSage | ex-Microsoft • AWS • Adobe | Teaching 70K+ How to Build Production-Grade GenAI Systems

I quit my dream job at Microsoft 5 months ago. And if someone asked me in January what 2026 would look like, I wouldn't have guessed this.

↳ Built a company from scratch. Solo.
↳ Designed and developed a production-grade AI engineering cohort. 35+ hours of on-demand content. Recorded, curated and structured every lesson myself.
↳ Built the entire GTM. Landing page, marketing, sales, payments, automations. All of it.
↳ Sold out the cohort in 10 days from launch.
↳ Built an AI teaching assistant bot with human-in-the-loop review. In half a day.
↳ Built an automated AI grading pipeline so every student got detailed, personalized feedback on their capstone submissions.
↳ Delivered 6 weeks of hands-on content to 50+ engineers from Microsoft, Adobe, Amazon, Shopify, Visa and more.
↳ Watched them build actual production AI systems. Legal compliance search, medical literature retrieval, enterprise SaaS documentation, security frameworks, financial regulation. Each one picked their own domain and built from scratch.
↳ Got featured in Forbes. (Yes, I know 🤭)

All solo. No team. No co-founder. No investors.

I'm not sharing this to flex. 
I'm sharing it because 5 months ago I had no proof any of this would work. 
I had an idea, a frustration with how AI engineering was being taught, and a bet on myself.

The bet paid off. 
And now I'm doing it again.

The April cohort is open. The curriculum is vetted by 50+ engineers with 10-15 years of industry experience. You don't just get a hands-on course project. 

You cross-apply every concept to your own domain, your enterprise problem, your own data. 6 live sessions with me. And you build all of this with a community of engaged fellow engineers.

Link is in comments.

♻️ Repost if this inspires someone to take the leap.

I just closed something I spent 8 months building. And if I knew this is how it's going to go, I would've quit much sooner.

I would have quit second-guessing the Engineer's RAG Accelerator even for a second. (What did you think? 😉)

After delivering my first cohort to 50+ engineers over the past 6 weeks, I have a newfound confidence that those 8 months were worth everything.

That confidence comes purely from what these engineers have been experiencing, saying and building week after week.

I built this cohort from a very personal pain I felt when upskilling in AI.

I wanted a practical, applicable, in-depth understanding of the domain in a structured manner. Not just the what and the why, but practical intuition on how to cross-apply these skills in production.

Without the hype and the noise.
Without overselling the magic and the power of AI.

By showing concretely what is possible and how to build systems that can scale.

And from that frustration of a sheer lack of such a resource for engineers, I created this for my fellow engineers who want to upskill holistically and start applying the learnings to the real world.

The only thing left for me to see: did this vision actually pan out for real engineers taking the course?

This is what 50+ engineers across Microsoft, Adobe, Amazon, Shopify, Visa and more experienced:

What they built: (Apart from the course project, everyone builds a capstone)

↳ Privacy-preserving legal search over SEC filings and contracts
↳ Clinical case study retrieval across 2,219 pages of medical literature
↳ Security compliance querying across CJIS, NIST, SOC 2 and HIPAA
↳ Enterprise vendor compliance analysis over DPAs from Google, Anthropic, OpenAI, Microsoft, AWS
↳ Recipe search from image-based PDFs using Vision AI extraction
↳ Academic concept mastery from MIT lectures, textbooks and handwritten notes
↳ Enterprise documentation search over 5,000+ SAP docs
↳ Azure architecture decision systems
↳ Financial regulatory knowledge assistants

What they said:

"It skips the fluffy AI hype and gets straight into the practical engineering reality. Feels like a senior engineer sitting next to you walking you through their thought process, rather than a disjointed corporate bootcamp."

"Without the evaluation framework, finding the best retrieval strategy was impossible. I would have gone with assumptions. With evaluation, it was pretty clear."

"This is one of the best decisions I have made. You have put your heart and soul into building this course, and that can be felt."

"100% worth it. The material and depth covered here are hard to find elsewhere."

And since the first cohort exceeded my expectations in every way, I have decided to conduct another, more polished version in April.

If you are serious about upskilling in AI and want to build production AI systems with your hands, not just read about them, the link is in the comments.

♻️ Repost to help someone find the right way to upskill.

AI engineering isn't vibe coding.
AI engineering isn't agentic AI.
AI engineering isn't context engineering.
AI engineering isn't MCP.
AI engineering isn't prompting models.

AI engineering is the disciplined practice of turning these components into one cohesive system.

It starts by understanding how each layer functions and connects.

1. The Core Unit: The LLM
↳ A powerful but stateless processing unit for language.
↳ Its job is to execute instructions based on the context you provide.
↳ Your job is to pick the right setup for your privacy, cost, accuracy and latency constraints.
↳ That one choice shapes how much work every layer after it demands.

2. The Control Signal: Prompts and Context
↳ A prompt is a runtime instruction, not just a question.
↳ In any serious system, prompts require a lifecycle:
→ Versioning: Treat them like code in Git.
→ Testing: Unit and regression tests to prevent breaks.
→ Registry: A central place to manage and deploy them.

↳ The model only knows what you give it per call.
↳ Which documents, which examples, what history, in what order? 
That's context engineering.

3. The Specialisation Layers: RAG and Fine-Tuning
↳ They solve different problems.
↳ RAG: Inject external knowledge at query time. Grounds the model in facts. Scalable, keeps up with changing data.
↳ Fine-Tuning: Teach new behaviour by modifying model weights. Expensive to maintain, hard to update.
↳ 70% of companies using generative AI augment models with retrieval systems.

4. The Data Foundation
↳ Your system is only as good as its data pipeline.
↳ Sourcing, cleaning, chunking, deduplication, vectorizing for retrieval. Curating examples for tuning.
↳ Representing data well requires understanding every layer above it.
↳ Making data LLM-ready across sources is where 95% of AI pilots break down 

5. The State Layer: Memory
↳ LLMs are stateless. Every request starts from zero.
↳ Memory turns a single-shot tool into a coherent application.
↳ Orchestrating the memory layer is crucial for context engineering.

6. The Feedback Loop: Observability and Evaluation
↳ If you can't trace a decision from input → prompt → retrieved context → generated output, you're debugging by guessing.
↳ Golden test sets, automated quality checks on every change, hallucination detection

7. The Constraint: Cost, Latency and Accuracy
↳ Every architectural choice is a tradeoff between these three.
↳ Reasoning models cost more. Efficient models handle less. Hybrid routing sends each query to the right class.

The tools change every quarter.
The buzzwords rotate every month.
Understanding how the layers connect
And engineering the system for success is the skill

__________
👋 P.S. In the first cohort of the Engineers' RAG Accelerator, 50+ engineers built production RAG systems from scratch in 6 weeks. Legal, medical, enterprise SaaS and more.

Want to learn AI engineering in a hands-on way?
April cohort is open (link in comments).

♻️ Repost to help someone see the full picture.

If I had to pick one playlist to build real intuition for how LLMs actually work under the hood, this is the only one I'd recommend.

10 lectures. ~20 hours. Free. 
By the end you will have built a GPT from scratch and reproduced GPT-2.

Most people will save this. 
Very few will actually finish it.

Neural Networks: Zero to Hero by Andrej Karpathy

1. Building micrograd (2h 25m)
Backpropagation from first principles.

2. Building makemore (1h 57m)
Your first language model. Character-level, bigram-based.

3. Building makemore Part 2: MLP (1h 15m)
Bigrams to multilayer perceptron. 

4. Activations & Gradients, BatchNorm (1h 55m)
Inside the network. Why deep nets were fragile. 
How batch normalization fixed it.

5. Becoming a Backprop Ninja (1h 55m)
Manual backpropagation. No autograd. 
Cross entropy, tanh, batchnorm.

6. Building a WaveNet (56m)
Tree-structured deep network based on DeepMind's WaveNet.

7. Let's build GPT (1h 56m)
Self-attention, transformer blocks, full GPT architecture.

8. State of GPT (42m)
Pretraining, SFT, RLHF. The full training pipeline explained.

9. Let's build the GPT Tokenizer (2h 13m)
Byte Pair Encoding from scratch. 

10. Let's reproduce GPT-2 124M (4h 1m)
Empty file to a trained GPT-2. The finale.

This playlist builds the intuition that no amount of documentation reading will give you. 

If you actually commit to finishing it, you will understand LLMs at a level most engineers never reach.

Watch here: https://lnkd.in/gd2JZ5Wt
______
👋 P.S. This playlist gives you the LLM foundation. But understanding how LLMs work is level 1 of the capability ladder.

What comes after is where most engineers get stuck:
↳ Grounding LLMs in real data through RAG
↳ Engineering the context that shapes every output
↳ Building evaluation pipelines that catch hallucinations before users do
↳ Adding memory, tool use and state management
↳ Shipping production systems with caching, observability and cost control
↳ Knowing when to add agentic workflows versus when simpler architecture solves the problem

That's levels 2 through 7.
That's what 50+ engineers from Microsoft, Adobe, Amazon, Shopify and Visa just built hands-on in 6 weeks in the Engineer's RAG Accelerator. 

Each one picked their own domain, curated their own data, and shipped a production-grade system from scratch.

April cohort is open (link in comments).

♻️ Repost to help someone start with the right foundation.

If I had to upskill in AI from scratch in a structured, hands-on way, this is the one resource I'd follow.

I've shared curated lists, playlists, and roadmaps before. 

But this time I built something bigger.
Before I share it, let me walk you through what it actually teaches:

Week 1: LLM Foundations + First RAG Pipeline
↳ How LLMs are actually built
↳ Tokenization, attention, pretraining, RLHF
↳ RAG project scoping framework
↳ Data ingestion and indexing pipelines
↳ Embeddings and vector databases
↳ Your first working RAG system on your own data

Week 2: Chunking + Evaluation Foundations
↳ Data deduplication and quality analysis
↳ Chunking strategies: fixed, recursive, semantic, AST
↳ Controlled experiments across strategies
↳ Intro to LLM-as-judge evaluation
↳ Embedding model selection and tradeoffs

Week 3: Advanced Retrieval
↳ Vector DB internals (HNSW)
↳ Dense vs sparse vs hybrid search
↳ Cross-encoder reranking
↳ Reciprocal rank fusion
↳ Metadata filtering and two-stage routing
↳ Cost, accuracy, latency tradeoff analysis

Week 4: Evaluation Mastery
↳ Golden dataset creation (bootstrapped)
↳ Synthetic generation and its limits
↳ RAGAS and DeepEval frameworks
↳ Deterministic metrics: P@k, R@k, MRR, NDCG
↳ Decomposed claim verification
↳ Triangulation across evaluation methods
↳ Production monitoring and evaluation flywheel

Week 5: Production Infrastructure
↳ Conversation memory and query rewriting
↳ Semantic caching with Redis
↳ Query classification and routing
↳ Type-specific prompt engineering
↳ Observability and tracing (Opik)
↳ FastAPI, SSE streaming, Docker deployment

Week 6: Intelligent Systems + Security
↳ AI system intelligence spectrum (5 levels)
↳ Corrective RAG (self-grading retrieval)
↳ Adaptive RAG (LLM-driven tool selection)
↳ Prompt injection and retrieval poisoning defence
↳ Context engineering as the unifying discipline

35+ hours of on-demand content. 
6 live sessions. A capstone project on your own domain, your own data, your own enterprise problem. 
Built with a community of engineers going through the same journey.

This is the Engineer's RAG Accelerator.

50+ engineers from Microsoft, Adobe, Amazon, Shopify and Visa just completed the first cohort. The curriculum is battle-tested.

To join the April cohort → https://lnkd.in/gCTA2tvp

♻️ Repost to help someone find the structured path.

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The Battle

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