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

Read more code.

And fewer articles, tutorials, or surface-level books.

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 code - 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 the rapid pace at which AI is going.
But this is a massive learning opportunity if you know where to look.

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

It happens when you:
- Read real production code
- Study PRs or commit history
- See how teams structure repos, 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 (subscribe if interested) https://buff.ly/2rwqlNL

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

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

I developed a multi-agent system (PoC) in a week at Microsoft's Hackathon; this speed is a result of months of upskilling. Here's how I'd start all over again.

After completing the hackathon, I realised the old rules don't apply.

Deterministic logic is replaced by probabilistic reasoning, and the real skill is taming the non-determinism.

Here’s the blueprint to get you started.

(P.S. I saved the most valuable one for the end)

LAYER 0: DL, NLP, and LLM Internals
Understand the engine first.

↳ Resources: 
• https://lnkd.in/gwqSPS4J
• https://lnkd.in/gd2JZ5Wt
• https://lnkd.in/g2pXsyHn
• https://lnkd.in/gPimp6mv

LAYER 1: Prompt Lifecycle Management
Your prompts are your control signals.

↳ Understand Patterns: Few-shot, Chain-of-Thought, ReAct.
↳ Implement a Lifecycle: Version control your prompts (Git), A/B test them, and use a registry for production deployment.

↳ Resources:
• https://lnkd.in/g2GfgaBp
• https://lnkd.in/gfByGMzt
• https://lnkd.in/g2RmYfKh

LAYER 2: RAG and Data Preparation

RAG is the memory and knowledge layer of your agent. It’s non-negotiable.

↳ Performant RAG depends entirely on data prep. 
↳ Context Engineering: This is about creating the perfect information package for your agent at runtime.

↳ Resources:
• https://lnkd.in/gTkNFRAz
• https://lnkd.in/gA7maM5Y
• https://lnkd.in/g2Tdc_h2

LAYER 3: Agentic AI Primitives
Before building a multi-agent system, master a single agent.

↳ Agent Capabilities: Understand tool calling, memory management, and planning loops.
↳ Protocols: Learn how standards like MCP enable modular and reusable tools.

↳ Resources:
• https://lnkd.in/ghcxpVXd
• https://lnkd.in/gbXxR4Dp
• https://lnkd.in/gfdJqGU4

LAYER 4: Multi-Agent Systems & Patterns

↳ Frameworks: LangGraph, AutoGen, and CrewAI are powerful, but the architecture comes first.
↳ Communication: Design how agents pass state, delegate tasks, and handle failures.

↳ Resources:
• https://lnkd.in/gY8buZ_c
• https://lnkd.in/gE_dMbYa
• https://lnkd.in/ga3SZAb3

LAYER 5: Evals and Observability

↳ You must be able to trace a decision from input → plan → RAG result → tool output → final response.

↳ Resources:
• https://lnkd.in/gcY8WyjV
• https://lnkd.in/gM4PyrMP

———
👋 PS: If you’re tired of scattered resources  and want a structured, hands-on path to shipping a real AI system,  I’m running a small cohort (50 engineers).
 
Details → https://academy.neosage.io
———

♻️ Repost this to help your network build the real thing.

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

Everyone wants to be an AI Engineer.
 But only a few understand what it actually takes to be one
 
What people think AI engineering about:
 
• Picking Claude Opus 4.5 vs GPT-5
• Jumping on MCP, A2A, “agents”
• Choosing LangGraph vs CrewAI
• Treating a vector DB as “RAG”
• Shipping demos without evals or guardrails
• Wrapping everything in the newest framework
• Assuming prompts can compensate for bad data
• Optimising for hype instead of reliability
 
But here’s what actually takes to build for prod:
 
→ Treating the LLM as a core computing unit: choosing the right model for cost, latency, and  reliability

→ Designing prompts like software: versioned, tested, and governed

→ Building RAG with rigour: data quality, retrieval evaluation, and attribution

→ Using fine-tuning only when necessary, and maintaining it like any other ML artifact

→ Owning the data pipeline: sourcing, cleaning, deduping, embedding, and indexing

→ Orchestrating the memory layer to ensure LLMs' success at the task

→ Instrumenting systems with observability + evals from day one

→ Tracing decisions end-to-end: input → retrieval → prompt → model → output

→ Treating AI as a system of interacting components, not isolated tools

→ Iterating in production with guardrails, tests, and feedback loops
 
Being an AI Engineer is less about frameworks or models
And more about data, system design, reliability, and scalability

———
PS: Want a structured, hands-on path to real AI Engineering?

I’m running a 50-seat cohort for engineers serious about shipping in production.
Learn alongside peers from Microsoft, Amazon, Adobe, Shopify, and more.  

Details → https://lnkd.in/gCTA2tvp
———

♻️ Repost to help your network build better AI systems

This is the 12-week roadmap I'd give any software engineer who wants to become an AI Engineer in 2026.

(P.S. There's something at the end for those who want to get hands-on)

---
WEEKS 1-2: Foundations

↳ How LLMs actually work: pretraining, post-training, and why they hallucinate
↳ RAG architecture and core components
↳ How to identify, qualify, and define your RAG project
↳ Setting up your production stack: vector database, orchestration framework, LLM API
↳ Build your first end-to-end pipeline

You're not optimizing yet.
You're building intuition.

---

WEEKS 3-4: Chunking, Embeddings, and Your First Evaluation

↳ Why chunking is your most important decision
↳ Embeddings deep dive: how text becomes vectors
↳ Chunking strategies: fixed-size, semantic, AST-based for code
↳ Introduction to evaluation: LLM-as-a-Judge and human review
↳ Hands-on: systematically compare strategies and find a winner

---
WEEKS 5-6: Advanced Retrieval Architectures

↳ Vector database internals: how HNSW and ANN algorithms work
↳ Dense vs Sparse retrieval: solving the coverage problem
↳ Hybrid retrieval with Reciprocal Rank Fusion (RRF)
↳ Reranking: bi-encoders vs cross-encoders
↳ Two-stage retrieval: LLM routing for precision
↳ The CAL Framework: Cost-Accuracy-Latency tradeoffs

---
WEEKS 7-8: Mastering Evaluation

Evaluation is harder than building RAG.
↳ Why RAG evaluation isn't like traditional ML
↳ Synthetic test set generation with RAGAS
↳ LLM-as-a-Judge evaluation with DeepEval
↳ Bootstrapped Golden Datasets: creating ground truth
↳ Choosing the right evaluation strategy for each iteration
No evaluation = shipping blind.

---
WEEKS 9-10: Production Engineering

Your system works. Now make it fast, reliable, and cost-effective.
↳ Production RAG architecture: latency, cost, observability
↳ Semantic caching: achieving 1000x+ speedup on repeat queries
↳ Production backend with FastAPI and streaming responses
↳ Integrating observability and tracing
↳ Smart retries, adaptive prompting, cache invalidation

---
WEEKS 11-12: Advanced Patterns and Deployment

Evolve from a RAG system to an intelligent application.
↳ Beyond basic RAG: Cache Augmented Generation and Agentic architectures
↳ Advanced query understanding: expansion, decomposition, multi-step RAG
↳ Dynamic retrieval: query routing and RAG-as-a-pluggable-tool
↳ Production deployment: Docker, cloud platforms, security
---

This roadmap gives you the what and the why.

But I'm running a 6-week cohort where we execute this together. Hands-on. With feedback. You ship a real system.

Cohort 1 starts Feb 9th.
50 spots. (60% sold out, in less than 48 hours)

If that's what you're looking for: https://academy.neosage.io

♻️ Repost to help an engineer in your network who needs this.

The Engineer's RAG Accelerator

Most people present RAG as:
Query -> Vector DB -> LLM -> Response.
That’s the tutorial version.

Here’s the mental model I use for production RAG:
5 layers of capability, from foundations to reliability.

𝟭) 𝗗𝗮𝘁𝗮 + 𝗚𝗿𝗼𝘂𝗻𝗱 𝗧𝗿𝘂𝘁𝗵 (𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻)

This decides retrieval quality before a user ever types a query.

Ingest -> parse -> chunk (semantic / structural) -> embed -> index.  
Your chunking strategy defines recall ceilings.  
Your embedding choice defines semantic resolution.

Evaluation starts here:
- Golden datasets for ground truth  
- Synthetic queries for scale  
- Distribution checks for drift  

If this layer is wrong, every downstream metric lies.

𝟮) 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹

This is where most RAG systems fail quietly.

Production retrieval is not “top-k vectors”.

It’s:
- Query embedding + metadata filtering
- Dense retrieval (semantic recall)
- Sparse retrieval (lexical precision)
- Score fusion / boosting
- Cross-encoder reranking

Every step has thresholds.  
Every threshold trades recall for precision.

Miss here, and generation never had a chance.

𝟯) 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻

Production prompting isn’t “paste context + ask question”.

It’s:
- Context selection, not accumulation  
- Token budgeting under latency constraints  
- Structured prompt assembly (system + task + evidence)  
- Streaming for perceived performance  

Too much context -> attention dilution.  
Too little context -> hallucination.

Generation quality is downstream of retrieval discipline.

𝟰) 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻

Not every query deserves the full pipeline.

This layer decides:
- Is retrieval needed at all?
- Which retriever configuration to run?
- When to retry, reroute, or degrade gracefully
- When to escalate to humans or tools

Request routing, intent classification, fallback paths.  
This is system design, not prompting.

𝟱) 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿

Observability isn’t optional.

You need visibility into:
- Retrieval scores and misses  
- Reranker shifts  
- Latency per stage  
- Answer quality over time  

Evaluation loops close the system:
offline -> online -> feedback -> improvement.

Without this layer, you’re shipping vibes.

Each outer layer depends on the inner ones.

Most LLM failures aren’t model problems.  
They’re data, retrieval, or orchestration failures.

———
👋PS: I’m running a 6-week Production RAG cohort focused on tying together all the layers: data, architecture, evaluation, and real-world tradeoffs.  
Details and link in the comments.
———

♻️Repost if this helped you see the full picture.

AI Engineer role is #1 on LinkedIn's fastest-growing jobs for 2026. If I had to go from zero to building agentic systems in 90 days, this is the exact learning arc I’d follow.  

(PS: I saved the best for the end)

You don’t “learn AI.” You build intuition layer by layer.

LLMs are just prediction engines.

Agents are what happen when you give them context, memory, tools, and a goal.
But most people don’t know how to bridge that gap.

Here’s your roadmap

𝟬. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 (Weeks 1–3)
Understand the moving pieces before you connect them.

• Karpathy “LLMs in an Hour”
https://lnkd.in/gJg77fqt

• How GPTs Are Born: Internet Feeding + Training Loops
https://lnkd.in/g2pXsyHn

• How GPTs Learn to Be Helpful
https://lnkd.in/gPimp6mv

• Stanford CS229 Lecture
https://lnkd.in/g-HCdnAm

• “Attention Is All You Need”
https://lnkd.in/gXUccydp

𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 (Weeks 4–5)
If you can’t guide the model, nothing else matters.

• OpenAI Prompt Engineering Guide
https://lnkd.in/gqjcPxcQ

• Prompt engineering for devs
https://lnkd.in/gHwMeZzp

𝟮. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 + 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 (Weeks 6–10)
Fine-tuning, retrieval, chaining, grounding, tool use.

• DeepLearning.ai Generative AI Course Guide
https://lnkd.in/gbdiAkPD

• LLMOps: Deploy, Observe, Iterate
https://lnkd.in/gS_tA8hJ

𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 (Weeks 11–12)
How to make agents reason, act, collaborate

• Google Agents Whitepaper
https://lnkd.in/ghcxpVXd

• ReAct: Reasoning + Acting
https://lnkd.in/gbXxR4Dp

• AutoGen Patterns Short Course
https://lnkd.in/gE_dMbYa

• LangGraph Short Course
https://lnkd.in/gmgtwpjf

• GenAI_Agents GitHub repo
https://lnkd.in/gqjNSzrW

𝟰. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗘𝘃𝗮𝗹𝘀 (Weeks 13–14)
If you can’t evaluate or debug your agents, you’re just playing pretend.

• Agent as a Judge
https://lnkd.in/gmiUe-nv

• Evaluating AI Agents
https://lnkd.in/gcY8WyjV

• Safe and Reliable AI via Guardrails
https://lnkd.in/g5CykhcH

This is the stack I wish I had earlier.

——— 
PS: Want a structured, hands-on path to real AI Engineering?  

I’m running a 50-seat cohort for engineers serious about shipping in production. Learn alongside peers from Microsoft, Amazon, Adobe, Shopify, and more.

Details → https://lnkd.in/gCTA2tvp 
———

♻️ Repost to help more folks build the real thing

I'm about to close something I spent 6 months building.
.
.
The Engineer's RAG Accelerator.

45 engineers from Microsoft, Amazon, Shopify, Revolut, Visa, Cisco, Autodesk just enrolled

We're down to the last 5 spots. Then we close enrollment (what did you think I was closing?)

Cohort 1 starts February 9th.

If you've been waiting for a structured way to get hands-on experience in AI Engineering and learn with a community of senior engineers, this is exactly for you.

Join the cohort here: https://lnkd.in/gCTA2tvp

I tested 7 chunking strategies on production RAG.

Here's what failed and what actually won:

→ Fixed-size: Fast but dumb. Splits sentences in half. Prototype only.

→ Recursive: The safe default. 80% of RAG apps use this. Works fine, rarely optimal. Might fail if your semantic endings are non-uniform

→ Semantic: Sounds smart. Costs 2x compute. Causes non-uniform splits and fails badly on uniform content (code, specs).

→ Parent-child (small-to-big): Retrieves small, returns big for context. Works if child chunks are highly targeted factoids. Has trouble scaling on docs with unclear boundaries 

→ AST code-aware (adapted to usecase often paired with Hybrid routing): Respects function and class boundaries. Winner for technical docs. +4.3% recall (if adapted for your usecase right).

The truth?

There's no universal winner.
What works for legal docs fails for codebases.
What works for FAQs fails for research papers.

You have to test on YOUR data.

That's why in the Engineer's RAG Accelerator, we implement 7 different strategies and evaluate them head-to-head with a real evaluation framework.
(We build the eval layer from scratch to A/B test all decisions reliably)

That's week 2 of the cohort.
40 Engineers (from organisations like Microsoft, Revolut, Visa, Autodesk and more) have already enrolled

Last 10 spots, filling fast

Link in comments.

♻️ Repost to share these insights with your network

Vibe coding won't make you an AI engineer.
Prompting won't make you an AI engineer.
Automations won't make you an AI engineer.

There’s an increasing demand for AI-literate professionals, especially in engineering.

And as much as people wanted to believe the “𝘈𝘐 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴” myth…

The road to AI engineering runs straight through software engineering.

Building software isn’t just about coding.
Building agentic applications isn’t just about calling an LLM with some data.

And 𝘷𝘪𝘣𝘦 𝘤𝘰𝘥𝘪𝘯𝘨? 
Definitely not the answer.

Don’t get me wrong, AI assistance is a massive boost.
But it’s exactly that: a boost, a multiplier.

It only works if you have the skill to steer it in the right direction.

To be a good AI engineer you need to:
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 model real-world problems in code
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 understand data systems
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 understand software systems
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 stitch systems, because a lot can break at the seams
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 choose between tradeoffs (everything is a tradeoff)
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 identify and solve system bottlenecks
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 upskill, because everything becomes obsolete within years
• 𝗞𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 learn on the job and still deliver quality

Only then do you see where it all clicks, 
because when you know how to build software systems, 
You see AI for what it really is: 

a 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 to interface with.
(Of course, one with plenty of baggage.)

Software engineering is a language.
If you can’t think in it, you can’t speak it.
When you 𝘤𝘢𝘯 speak it, AI just becomes 𝗻𝗲𝘄 𝘃𝗼𝗰𝗮𝗯𝘂𝗹𝗮𝗿𝘆.

___

👋 PS: Want the structured path from software engineer to AI engineer?

That's what the RAG Accelerator is for.
Engineers from Microsoft, Adobe, Amazon, Atlassian... already enrolled.

Join the cohort here → https://lnkd.in/gCTA2tvp 
___

♻️ Repost this if it struck a chord with you

8 things Every AI Engineer Should Know

Everyone thinks AI Engineering is about Agents.
But agents are just layer 6 of 20.
Here's the full application stack every AI team should understand.                                   

You won’t need every layer,
But knowing what exists is crucial

0/ Data Versioning & Lineage
Tracks every dataset end-to-end, guaranteeing reproducible training, audits, and instant roll-backs.

1/ Data Ingestion & Prep
Connects to raw sources, parses, dedupes, chunks, and tags data so downstream components see clean, metadata-rich text blocks.

2/ Embedding / Vectorisation
Turns text, code, images, or audio into high-dimensional vectors that capture semantics for retrieval, clustering, and model input.

3/ Vector Store & Retrieval
Persists embeddings with metadata and powers fast similarity or hybrid search

4/ Model Repo & Management
Versions base checkpoints and fine-tunes; handles access control, production promotion, and roll-backs.

5/ Serving Runtime
Runs inference with batching, caching, and GPU scheduling to hit sub-second latency and maximise hardware throughput.

6/ Orchestration / Agents
Chains model calls, tools, and memory; builds stateful or multi-agent workflows that reason and act autonomously.

7/ Agent & Tool Communication Protocols
Standard schemas and handshakes that let cross-vendor agents collaborate securely.

8/ Fine-Tuning & Adaptation
Applies LoRA/QLoRA, instruction tuning to specialise foundation models

9/ Experiment Tracking & Registry
Logs hyper-params, metrics, and artefacts, compares runs, and registers the “blessed” model for deployment.

10/ Evaluation & Testing
Provides automated relevance, faithfulness, and regression checks

11/ Observability & Cost Guard
Captures token-level traces, latency, error rates, drift, and spend

12/ Safety & Guardrails
Enforces content policies, detects jailbreaks/PII, validates output schemas

13/ CI/CD & Pipeline Orchestration
Automates retraining, nightly evals, and zero-downtime rollouts in a reproducible, version-controlled workflow.

14/ Low-Code Automation
Trigger-based glue that lets non-engineers wire GenAI outputs into business processes.

15/ Deployment Infrastructure
Handles containers and GPUs with autoscaling and multi-region failover for high availability.

16/ User Experience
Front-end chat, dashboards, and APIs that deliver responses and capture feedback for continuous improvement.

17/ Multimodal Generation / Understanding
Adds vision, video, or speech

18/ Model Routing & Fallbacks
Smart broker that selects the best model by cost, latency, or capability, and cascades to fallbacks on failure.

19/ Prompt Management & Versioning
Treats prompts as code: version controls, A/B tests, and rolls back prompt changes.

📌 Save this for reference.

_____
P.S. This systems-level thinking is exactly what we cover in the RAG Accelerator.

6 weeks. Production-grade. Engineers from Microsoft, Adobe, Amazon already enrolled. 
Waitlist for April cohort → https://lnkd.in/gCTA2tvp 
_____

I just launched the structured learning path to AI Engineering, for experienced engineers, I wish I had when I first started.  

It's the Engineer's RAG Accelerator (more on this in a bit, first)
 
Let me tell you how we got here.

I started NeoSage as a newsletter to bridge the gap between the research layer and the application layer.

I wanted to focus on 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝗼𝗻-𝗳𝗶𝗿𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 that applies to actual enterprise use cases.

When I started sharing my notes here, 75,000+ of you: Engineers, PMs, and CTOs, started following along. 

And then the DMs started pouring in.
Hundreds of them. People asking for guidance, for resources, for a path forward. 

I tried my best to respond to everyone, but honestly? I couldn't keep up.

But here's what I realised through all those conversations:

There's a massive gap between the learning material out there and what actually translates to products that can ship. 

Tutorials get you started. They don't get you to production.

𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿'𝘀 𝗥𝗔𝗚 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 is my answer to all of this.

It's a 6-week intensive, cohort-based experience designed to take you from the ground up: 

understanding LLM fundamentals, to building, evaluating, shipping, and maintaining end-to-end RAG (and AI) systems.

𝗪𝗵𝘆 𝗥𝗔𝗚? (I know I sound like a broken record). 

But RAG is non-negotiable. 
Every AI application needs some adaptation of this pattern. 

No AI engineering interview is complete without it. 
This is fundamentally where you need to start.

Since I opened enrollment to the cohort waitlist 18 hours ago, we've already sold 23 of 50 seats.

Why only 50? 

Because I want to keep this high-touch and engaging. 
A cohort where I can actually focus on outcomes for every engineer who joins.

Here's what you can expect:
→ 45+ hours of production-grade RAG content
→ Industry tech stack: Haystack, Qdrant, Gemini, Redis, FastAPI
→ Weekly live office hours as a group
→ Daily cohort chat support
→ Community of engineers learning with you
→ Build your evaluation layer from scratch (RAGAS, DeepEval, custom metrics)
→ A portfolio-ready capstone project
→ Certificate included
→ Learning and development fund friendly (get it expensed by your org, a lot of our students are)

Cohort 1 kicks off on February 9th.
Only 50 seats, more than 40% gone

Got questions? Ask me in the comments.

Ready to lock in your spot? 
https://academy.neosage.io

♻️ Repost this to share with everyone in your network

If I had to bet on one skill in AI as an engineer…
I'd go for RAG. And these are the resources I'd pick first to self-learn this in 2026.

Why RAG?
Read this https://lnkd.in/geW3Xw_P

→ 1 paper: https://lnkd.in/gp7y4zFu
→ 1 blog: https://lnkd.in/gTkNFRAz
→ 1 tutorial: https://lnkd.in/gneRRxg9
→ 1 repo: https://lnkd.in/gA7maM5Y
→ 1 course list: https://lnkd.in/gPQMJVF9

That's the free path. It's a good place to start.

But here's what it won't give you:
→ How to turn messy enterprise data into RAG-ready formats
→ Evaluation frameworks (RAGAS, DeepEval, golden datasets)
→ Production patterns: caching, observability, cost-accuracy-latency tradeoffs
→ A community of senior engineers from Microsoft, Shopify, Revolut and more building alongside you
→ Live weekly office hours with me
→ A capstone you can actually deploy

That's what the Engineer's RAG Accelerator is.
6 weeks from foundations to production

36 engineers are already enrolled.
14 spots left. (Filling Fast)

Enroll Now: https://academy.neosage.io
---

Save this. Share it. Build systems that don't hallucinate for no reason.

♻️ Reposting this helps everyone in your network upskill

Thrilled to announce that Engineers from Microsoft, Revolut, Shopify, Visa, and Cisco just enrolled in my AI Engineering cohort. ...

35 experienced engineers in 3 days.

Here's where they're coming from:

→ Technical Consultant at Microsoft
→ Senior Data Scientist at Revolut
→ Senior Engineering Manager at Autodesk
→ Senior Software Engineer at Shopify
→ Technical Leader at Cisco
→ Systems Engineer at Visa
→ Senior Product Manager at Walmart Global Tech
→ Principal Engineer at Cubic Telecom
→ Principal Applied Scientist at Jupiter Money
→ Staff Software Engineer at Skylight
→ Data Engineering Lead at DT One
→ Director of Technology Strategy at Believ
→ Associate Professor at West Virginia University

These are experienced engineers who build systems at scale.

They're not looking for another beginner AI tutorial.
They're not looking to "learn Python for ML."

They want a structured path into AI engineering, one that starts from the fundamentals and ends with the ability to build production systems for their organizations.

That's exactly what the Engineer's RAG Accelerator offers.

Here's what you're signing up for:

→ LLM fundamentals: how they work, why they hallucinate, and what RAG actually solves

→ Data ingestion: turning messy, real-world enterprise data into RAG-ready formats

→ Retrieval deep dive: chunking, embeddings, hybrid search, reranking, two-stage architectures

→ Evaluation-driven development: build your eval layer from scratch with RAGAS, DeepEval, and golden datasets

→ Production engineering: cost-accuracy-latency tradeoffs, caching, orchestration, observability, security

→ A capstone project you can deploy and show your team

→ Learn with a community of experienced engineers from around the world

6 weeks. 45+ hours of content.
Weekly live office hours with me.
Daily cohort support.

You don't need AI experience to join.
You need engineering experience and the drive to build.

By the end, you'll have the skills to start building RAG systems for your own organization, systems that actually scale.

Only 15 spots left out of 50.
Enrol Now: https://academy.neosage.io

Got questions? Drop it in the comments or DM me, happy to answer each one

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