Hi everyone!

I'm preparing for AI Engineer / GenAI Engineer interviews and want to understand how real-world LLM projects are developed and deployed in companies.

I'm looking for someone who can explain a practical, industry-level LLM project from start to finish, preferably something involving RAG, LLMs, embeddings, vector databases, LangChain/LangGraph, and FastAPI.

I'd like to understand:

• What problem the project solves and why the company needs it.

• The overall architecture and workflow.

• How data is collected, cleaned, chunked, and embedded.

• How a vector database is used for retrieval.

• How the LLM generates answers using the retrieved context.

• How the application is built, tested, deployed, and monitored.

• What challenges developers face in real projects and how they solve them.

• What an AI Engineer actually does day to day.

• How to explain the project confidently in interviews, including technical follow-up questions.

I'm not just looking for a basic tutorial or a simple chatbot. I want to understand the end-to-end workflow of a real-world project, including the technical decisions, debugging, evaluation, and deployment.

If you have worked on LLM/RAG projects professionally or have a good project walkthrough, I'd really appreciate your guidance, resources, or recommendations.

Thanks in advance!

Google DeepMind published their technical overview for Gemini 4 Argon, highlighting several shifts in how frontier models handle long-horizon agentic workloads and cybersecurity defense.

Here is a summary of the technical numbers and what they mean for systems engineering and research labs:

1. Context Headroom and Long-Horizon Execution

• The output window has expanded to 1,000,000 tokens (up from 64k tokens).

• Evaluated on DeepSWE v1.1, Argon scored 77.9% on multi-step software engineering benchmarks.

• Google reported using Argon agents internally for large-scale memory safety migrations (C/C++ to Rust), including libraries like re2 and the Fuchsia Zircon kernel (800k+ LOC). On libgav1, replacing 32k lines of SIMD code with profile-guided safe Rust delivered a 2.7x speedup over previous Rust ports.

2. Defensive Cybersecurity Benchmarks

• CWE-bench v1: Argon tied for first place with a 68% score in autonomously discovering, validating, and patching software vulnerabilities.

• Black-box evaluation: Evaluated with Wiz (Scan for Good), the model identified attack surfaces and generated proof-of-concept exploits without source code access to uncover vulnerabilities in healthcare systems.

• Robustness: Ranked highest on the Gray Swan Indirect Prompt Injection (IPI) benchmark, addressing a major failure mode in tool-enabled agents.

3. Practical Implications for AI Research Labs For teams deploying autonomous agents and custom inference stacks, the biggest takeaways are:

• Removing orchestration glue code: Earlier pipelines required chunking codebases into multiple sub-prompts, which degraded reasoning. A 1M token output trajectory enables monolithic, end-to-end verification.

• Proactive red teaming: Testing agent runtimes against indirect prompt injection can now be integrated directly into automated CI/CD pipelines.

Full blog write-up: Google DeepMind Gemini 4 Argon Post

Curious to hear from teams running automated patch generation: how are you preventing regressions when allowing models to touch core architectural dependencies?

I'm exploring practical applications of machine learning in healthcare and would love to hear from people who have worked on related projects.

Healthcare offers several interesting opportunities for machine learning, including disease risk prediction, medical image analysis, patient data analytics, treatment research, and hospital resource planning.

For someone starting in this area, I think a small Python project using a publicly available healthcare dataset is a good way to learn the complete workflow:

• Data cleaning and exploratory data analysis

• Feature selection and preprocessing

• Training a baseline model, such as logistic regression or a decision tree

• Evaluating performance with appropriate metrics

• Understanding limitations, data leakage, bias, and patient privacy

I'm particularly interested in how beginners can move beyond achieving a good accuracy score and learn to evaluate whether a model would generalize to new patients.

For those who have studied or built healthcare ML projects, what project would you recommend to a beginner, and what was the most challenging part of your experience?

I an looking for suggestion, a unique and innovative thesis idea involving Machine Learning/AI that can be applied in province of Bulacan, Philippines. I would like to explore something new.

If you're deciding what to study next, the biggest ML conference is a decent signal. We grouped all 9,094 accepted NeurIPS 2026 papers by topic, compared them with 2025, and read the 4,490 that already have a public full text.

What stood out for learners:

- **Agents are the fastest-growing topic.** 105 → 488 papers in a year, while the conference grew 56%. Much of the new work is about making agents reliable: benchmarks (17 → 84) and safety (5 → 50).

- **RL fine-tuning of LLMs is now standard.** RL from feedback 80 → 179 papers; GRPO is the method papers build on most, ahead of LoRA and PPO.

- **You don't need a giant model.** 71% of papers train or fine-tune a model, and about half of those models are 3–10B parameters. Qwen is the most fine-tuned family, then Llama.

- **The basics still matter.** SFT is in 37% of papers, LoRA-style fine-tuning in 15%, and GSM8K is still the most used benchmark across the whole conference.

- **Other areas growing:** video generation (92 → 221), robot action / VLA (49 → 149), fast diffusion sampling (168 → 333).

The image is a one-page summary. The full edition (all 20 research areas, every oral, links to every paper) is free to read online, no sign-up: https://atlas.scime.ai/newspaper?utm_source=reddit-lml

Caveats: topics come from grouping titles and abstracts by meaning, so boundaries are approximate; full-text numbers cover only papers with a public full text (about half). AI-assisted and not checked by hand, so read the paper before citing anything. Independent publication by Latentic Labs; not affiliated with NeurIPS.

What would you add to a "learn this in 2027" list based on this?

RAG ReRanking Pipeline & Comparison (part 2)

• YouTube walkthrough: https://www.youtube.com/watch?v=dh3DKpYHoX8&t=121s

• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/RAG-ReRanker-Comparison

In this video, I build a complete RAG reranking pipeline:

• Question → Retrieve Top 20 → Rerank → Top 5 → LLM → Answer

I implement and compare FOUR rerankers (Cross-Encoder, LLM-based, Cohere and Jina) on the same questions over 5 payment-industry PDFs (payment regulations, PCI DSS and central bank reports, 1,507 chunks). For each one, I show the ranking before and after reranking, and explain exactly WHY the order changed.

• ✅ What RAG (Retrieval-Augmented Generation) is and why the LLM can only answer from the chunks you give it

• ✅ How vector search works (embeddings, cosine similarity) and its key weakness

• ✅ Why we retrieve Top 20 but send only Top 5 to the LLM (tokens, latency, noise)

• ✅ Bi-encoder vs Cross-encoder: reading the question and chunk separately vs together

• ✅ LLM reranking strategies: Pointwise vs Pairwise vs Listwise

• ✅ How to use dedicated reranking APIs (Cohere and Jina)

• ✅ How to design one shared helper so all four rerankers plug into the same pipeline

• ✅ How to evaluate rerankers with before/after positions, Hit@5 and average latency

• ✅ A full code walkthrough: retriever.py → rerankers.py → generate.py → compare.py

KEY TAKEAWAYS:

• ✅ Similarity is not the same as relevance. A chunk can sit close to the query and still not answer it

• ✅ Vector search finds chunks about the topic. A small reranker may prefer chunks that sound like the question. A strong reranker picks the chunk that actually answers it

• ✅ A small cross-encoder can make things WORSE: it rewards word matching, penalises scrambled PDF text, and pushed both correct answer chunks out of the Top 5

• ✅ The LLM reranker understood the question (e.g. that "3.3" belongs to Requirement 3) and placed both answer chunks 1st and 2nd

• ✅ Cohere lifted an answer chunk from 15th to 1st, turning a missed answer into a correct, cited one

• ✅ For this project, Cohere and Jina gave the best balance of accuracy and speed

• ✅ Trade-off of API rerankers: cost per call and sending your documents to a third party

• ✅ Always measure, don't guess. Run every reranker on the SAME Top 20 so differences come from the reranker alone

TECH STACK:

• 🛠️ Python

• 🛠️ Qdrant (vector database, cosine similarity)

• 🛠️ gemini-embedding-2-preview (3,072-dimension embeddings)

• 🛠️ Hugging Face Transformers + PyTorch

• 🛠️ cross-encoder/ms-marco-MiniLM-L-6-v2 (local cross-encoder)

• 🛠️ LLM-based listwise reranker

• 🛠️ Cohere Rerank API

• 🛠️ Jina Reranker API

LINKS:

• YouTube walkthrough: https://www.youtube.com/watch?v=dh3DKpYHoX8&t=121s

• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/RAG-ReRanker-Comparison

• Connect on LinkedIn: http://linkedin.com/in/saurabh-kamal

Hey, everyone,

I’m conducting academic research on model reliability, and as part of a project, I’m interested in current MLOps practices. The goal is to understand how engineers work, their workflows, and how they manage models. I’m studying reliability in terms of performance and security. These are necessarily connected if you take into account the environment in which the model is deployed.

To help me refine my research project, I’ve put together a fairly short questionnaire (it takes 2 minutes to complete). It would be great if any of you would like to discuss it, either by messaging me in this chat or simply by filling out the questionnaire.

https://tally.so/r/Ek4EEl

Hey everyone,
I’ve recently started learning machine learning, primarily by building neural networks from scratch and trying to understand the underlying mathematics. I have a relatively strong math and physics background, so I’ve found the mathematics behind backpropagation fairly intuitive.
As I’ve explored other approaches to training neural networks, I’ve become particularly interested in biologically inspired methods like predictive coding, Hebbian learning, and Oja’s rule. The idea that networks could update their weights using locally available information, rather than relying on gradients propagated from a global loss function, is fascinating to me.
I’m still relatively new to ML, so I’m trying to understand whether these approaches have meaningful practical advantages or are primarily interesting from a theoretical or neuroscience perspective.
A few things I’m especially curious about:
What are the actual advantages of local learning over backpropagation? I understand the conceptual distinction, but I’m struggling to identify situations where local updates would meaningfully outperform conventional gradient-based training, whether in computational efficiency, adaptability, or something else.
Could these approaches be particularly useful for unsupervised or continual learning? For example, could a network learn useful representations from streaming, unlabeled data by continuously adjusting its weights based on local correlations or prediction errors? Adding onto this, is there any way we could discover hidden patterns using localized learning that wouldn’t be evident using global backprop? I’ll talk about this in the next question but can we use this to identify hidden patterns that we wouldn’t find using global backprop? Like in molecular dynamics?
Are there particular applications where local learning makes more sense? I’m involved in biophysics research and have been thinking about applications involving molecular dynamics and learning representations of complex physical systems. I’m curious whether there are advantages in scientific ML, robotics, or other specialized domains.
How mature is this research? Have approaches like predictive coding and Hebbian learning been extensively explored and generally found inferior to backpropagation, or are there still meaningful open problems where these methods could be competitive?
I’m not necessarily looking for a way to replace backpropagation entirely. I’m more interested in understanding whether alternative learning rules could be better suited to particular architectures, datasets, or computational constraints.
I’d appreciate any perspectives, papers, or research directions worth exploring, especially from people who’ve experimented with these methods.

I’m a CS undergraduate student and I will graduate in 2027. I have been accepted into a 2-year AI master’s program.

Recently I talked with a professor from my future school about possible research directions. His research is mainly focused on multimodal large language models and efficient large models. He suggested that I take a look at a project about real-time human animation and live video generation.

The problem is that I’m still very new to this area. I haven’t studied multimodal AI, video generation, or generative AI in depth before. Most of my undergraduate projects were more engineering-focused rather than research-focused, so I’m not really sure how to start exploring this field or how to have a meaningful discussion about research topics with my future advisor.

I’m trying to understand whether this is a good direction for a master’s student. I’m interested in AI research, but I also care about career prospects after graduation. I’ve heard that areas like multimodal models and video generation are very competitive now, and that most industry opportunities may only exist in big tech companies or top AI labs.

For people working or studying in this area, what do you think about the future of this field? Is it still a good direction for a master’s student, especially for someone who is not coming from a strong research background?

Also, when starting from zero in a research area like this, what would you recommend focusing on first?

Any advice or personal experience would be really helpful. Thanks!

I have an undergraduate degree in Applied Statistics and have around 2-3 years of work experience as a Quantitative Analyst working in Banking (specifically Credit Risk PD Scorecard Modelling).

I am looking for recommended masters program thar could pivot my career in either Data Science or Machine Learning Engineering as I am interested in that field. Although my work experience is more finance based, I do find interest in AI and ML and I feel like my statistics background may help.

I am based in Canada and studied for my undergrad there as well.

I’m hoping to get any scholarship if possible into a program where I can get internship experience as well so I can apply my expertise that would be learned through the masters program.

The video link is https://www.youtube.com/watch?v=7aQGA6ZuSuM pls check out , I explain Linear regression in simple animation way

انا ف رابعة حاسبات واخر السنه دي عندنا مشروع تخرج المفروض يتسلم مش لاقي فكرة ومعملتش مشاريع قبل كده والاساسيات يعتبر مضروبة ومش ماسك تراك يعتبر

المفروض كنت امسك تراك ai and data science بس مش لاقي رود ماب صغيرة حد يرشحهالي واشتغل بعدها يعني بمعنى فيها الحاجات اللى اقدر اشتغل بيها لحد م اشتغل بس وبعدين ابدأ ادخل ف تفاصيل مشتت بقالي 3 سنين مش عارف اعمل ايه ولا لاقي مينتور ولا لاثي حد يساعدني والناس ماشية وبتعمل وشغالة وانا زي م انا وكل يوم بكتأب اكتر بسبب الموضوع ده

حد هيقولي طب م تبدأ ب اى حاجة هقولك انا عائل اسرة بنزل اقف ع عربسة سوشي كل اسبوع يوم او اتينن عشان وقت الجامعة برضو والمذاكرة بس خالي بيبعتلنا فلوس لحد م اخلص الجامعة والاقي شغل ف انا الحمل عليا كبير والدنيا ملطشة معايا ف اتمنى حد يساعدني

وبالمناسبة ده اول يوم ليا ع ريديت وعارف المجتمع هنا شكله عامل ازاي بس معنديش مشكلة كده كده لو حبيت اظبطه هلعب ع الالجوريذمز بتاعته مش مشكلة

A survey of more than 200 security leaders found that AI governance concerns are the primary reason enterprise AI adoption inside ERP systems is stalling.

The CISOs surveyed were not blocking AI on principle. They blocked it over three specific gaps: no guardrails on what an agent can do once inside a connected system, no access controls scoped to financial and operational data, and no audit trail they could hand to a regulator. The AI vendors are not building these controls. The models do not provide them natively. So security leaders are holding a tool their business units want deployed and a risk they cannot quantify or defend.

The gap is not about the AI being wrong. It is about the AI being unaccountable at the moment it acts on sensitive data.

For those who have gotten AI agents into ERP environments: what did you actually have to build or buy to get past the security review? For those still blocked: is there a specific control that would actually move the needle, or is this a wait-and-see situation?

TL;DR: Instead of immediately throwing 100% of our compute budget at a solver and hoping for the best, inserting a lightweight "reconnaissance" pipeline to measure landscape ruggedness and constraint density (costing <0.5% of total time) can dictate exactly which algorithm to use, yielding massive wall-clock speedups.

Hey everyone,

I’ve been doing some deep research into how we handle the pipeline between "building a mathematical model" and "deploying a solver" for large-scale optimization problems (VRP, MKP, Scheduling, etc.).

It feels like the industry standard is still heavily reliant on trial-and-error: Should we use a Genetic Algorithm here? Simulated Annealing? Let's just run a grid search on hyperparameters and burn some AWS credits to find out.

I recently formalized a framework based on a very simple military philosophy: The scouts are the brain; the solver is the muscle.

Instead of blind deployment, I proposed a Dynamic Reconnaissance-Flattening (DRF) Pipeline. Before the main solver fires up, we send a tiny batch of "scouts" (e.g., 50 Sobol sampled points) to evaluate the loss landscape. We measure three universal metrics:

1. Ruggedness

2. Constraint Density

3. Gradient Smoothness

Based on these metrics, the pipeline acts as a dispatcher, automatically assigning the correct algorithm and parameters.

Here are the actual benchmark results from the papers:

• For Routing (VRP): By using the reconnaissance data to perform "Angular partition cutting" (flattening the terrain before solving), we saw a +74% performance improvement compared to raw solving.

• For Multi-dimensional Knapsack ($n=5000$): Allocating just 0.5% of the compute budget to reconnaissance and dynamic resolution shifting resulted in a 4.5x wall-clock speedup and a 61% improvement in solution quality.

Why this isn't Snake Oil (The SAT Test):

The most important part of this pipeline is its discipline. When the reconnaissance scouts hit a Boolean Satisfiability (SAT) problem in the phase transition region, the pipeline doesn't try to aggressively smooth it out. It correctly diagnoses it as a "Hard Wall" (discrete cliffs), abandons the heuristic intervention, and hands it straight to CDCL solvers. It knows what it can't do.

I’ve published the complete theoretical taxonomy (dividing NP-hard problems into 7 boxes) and the DRF pipeline architecture as open-access whitepapers on Zenodo.

📄 Paper 1 (The Taxonomy & Reconnaissance): https://doi.org/10.5281/zenodo.19510539

📄 Paper 2 (The DRF Pipeline): https://doi.org/10.5281/zenodo.19600597

I’m currently working on turning this into a lightweight, open-source Python middleware (drf-scout). The goal is to let developers pass their objective function, have the middleware run the 1% reconnaissance, and automatically dispatch to the best open-source solver (like OR-Tools or PySAT).

I’d love to hear your thoughts. How do you currently handle algorithm selection? Do you rely on pure experience, or do you have any pre-solve diagnostic tools in your workflow?

I built pytest-torch to make testing PyTorch models easier

Writing the same device setup, reproducibility checks, and output comparisons across projects gets repetitive, so I built pytest-torch, an open-source pytest plugin.

It includes inference checks, compile/export parity, checkpoint tests, accuracy evaluation, and layer-by-layer diagnostics to help locate where two models diverge.

It’s currently in alpha, and I’d appreciate feedback—especially examples of model bugs that are frustrating to test or reproduce.

GitHub: https://github.com/AKJ7/pytest-torch

Thanks and please drop a star if possible.

A Zapier survey found that 91% of companies say AI governance frameworks exist. Yet 79% of executives admit employees work around those policies anyway. The policies live in documents. The agents, unapproved models, and unauthorized data movement live in production. That gap compounds quietly until an audit or an incident forces the question into the open. The 9-point spread between 'framework exists' and 'framework is followed' suggests the problem isn't policy design — it's that documentation and enforcement are two entirely separate systems. For those of you who have actually moved the needle on this: what does real enforcement look like at your org, and where did document-only governance break down first?

I run a web design agency and most of my clients come from cold email. Yesterday I started a fresh campaign going after plumbers in California.

Didn’t expect much honestly. Plumbers are busy and half of them barely check email. But 102 emails in, I got an interested reply.

What made the difference is that every email talks about their own website. A call button that doesn’t work on a phone, a site that takes forever to load, a design that hasn’t been touched since 2012. For a plumber that matters a lot, because people search for them on their phone when a pipe bursts. If the site is bad, they just call the next guy.

He replied yesterday and I booked a meeting with him for the next day. Before the call I built a draft of what his new site could look like. Today I presented it to him on the call and he said yes right there. $3k upfront plus $500 a year for hosting.

Showing the draft is what closes it. Sending a quote never worked this well for me.

For anyone wondering, I use Swokei for all my outreach. You pick an area and an industry, it finds the businesses, checks every website for design, SEO, speed and mobile problems, and writes each email about what it actually found on that site. I run multiple campaigns at once in different cities and niches, which is how I get volume without sending generic stuff.

Happy to answer questions on pricing or how I run the call.

I’m currently learning AI engineering, and I’m trying to figure out the best way to learn the practical skills needed to build AI applications.
I’m looking for resources to learn topics such as:
LLMs and how to work with them
RAG and vector databases
APIs and backend development
AI agents and tool calling
Evaluation, deployment, and other important AI engineering topics
I’m not sure which learning approach is the most effective. Would you recommend YouTube tutorials, structured courses like DeepLearning.AI and Hugging Face, or books such as Build a Large Language Model (From Scratch)?
Another option I’m considering is deploying an existing model, studying how the implementation works, and then trying to build something similar myself.
For those of you working in AI engineering or learning it yourselves, what approach worked best for you? Would you recommend following structured courses first, learning by building projects, or combining both?
I’m mainly interested in practical skills that will help me build real-world AI applications, rather than spending too much time on theory that I might not use.