Key Elements of AI

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

Building a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗟𝗟𝗠 𝘀𝘆𝘀𝘁𝗲𝗺 is hard. In LLM world practicing your Systems Thinking is key, as much as when building any other complex software system. Here are some of the moving parts in the RAG based systems that you will need to take care of and continuously tune in order to achieve desired results: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: 𝘍 ) Chunking - how do you chunk the data that you will use for external context. - Small, Large chunks. - Sliding or tumbling window for chunking. - Retrieve parent or linked chunks when searching or just use originally retrieved data. 𝘊 ) Choosing the embedding model to embed and query external context to/from the latent space. 𝘋 ) Vector Database. - Which Database to choose. - Where to host. - What metadata to store together with embeddings. - Indexing strategy. 𝘌 ) Vector Search - Choice of similarity measure. - Choosing the query path - metadata first vs. ANN first. - Hybrid search. 𝘎 ) Heuristics - business rules applied to your retrieval procedure. - Time importance. - Reranking. - Duplicate context (diversity ranking). - Source retrieval. - Conditional document preprocessing. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: 𝘈 ) LLM - Choosing the right Large Language Model to power your application. ✅ It is becoming less of a headache the further we are into the LLM craze. The performance of available LLMs are converging, both open source and proprietary. The main choice nowadays is around using a proprietary model or self-hosting. 𝘉 ) Prompt Engineering - having context available for usage in your prompts does not free you from the hard work of engineering the prompts. You will still need to align the system to produce outputs that you desire and prevent jailbreak scenarios by Guard-railing inputs and outputs. What other pieces of the system am I missing? Let me know in the comments 👇 #LLM #GenAI #LLMOps #MachineLearning

Check out this 8-Layer Architecture for LLM Systems Large Language Models (LLMs) are more than just massive neural networks, they’re complex multi-layered systems built for performance, reliability, and scalability. Each layer plays a unique role; from managing raw data and embeddings to deployment and safety. Together, they form the backbone of how modern AI operates in real-world environments. 1. Infrastructure Layer The foundation of LLMs, handling compute power, networking, and storage across CPUs, GPUs, or TPUs. 2. Data Processing Layer Focuses on data ingestion, cleaning, tokenization, and sampling, which turns raw data into training-ready datasets. 3. Embedding & Representation Layer Transforms words into numerical embeddings for semantic understanding using techniques like positional encoding and PCA. 4. Model Architecture Layer Defines the core neural network structure which includes attention heads, normalization, and architecture design for token prediction. 5. Training & Optimization Layer Handles pretraining, fine-tuning, and distributed optimization for model performance and scalability across datasets. 6. Alignment & Safety Layer Ensures models align with human values and ethics through reinforcement learning, feedback loops, and safety policies. 7. Evaluation & Serving Layer Manages testing, inference, and model evaluation pipelines, ensuring reliability and real-world performance consistency. 8. Deployment & Integration Layer Covers API deployment, SDKs, monitoring, and analytics, bringing the model into production environments. To summarize, each layer in the LLM architecture contributes to a balanced system that enables real-world integration. However, this doesn’t come without challenges. #LLM

My biggest takeaways from Aishwarya Naresh Reganti and Kiriti Badam on building successful enterprise AI products: 1. AI products differ from traditional software in two fundamental ways: they’re non-deterministic, and you need to constantly trade off agency vs. control. Traditional product development processes break when your product gives different answers to the same input and can do things on its own. 2. The agency-vs.-control tradeoff is the core design decision in every AI product. Aish and Kiriti frame this as a spectrum: on one end, the AI acts autonomously with minimal guardrails; on the other, the system is tightly constrained with explicit rules and human-in-the-loop gates. Most successful enterprise AI products land somewhere in the middle, dynamically adjusting control based on confidence scores, context, and risk. 3. Most AI product failures come from execution missteps, not model limitations. Aish and Kiriti see teams blame the underlying LLM when the real issue is unclear product scope, missing guardrails, or poor user onboarding. A model that hallucinates 5% of the time can still power a great product if you design the UX to surface confidence scores, let users verify outputs, and constrain the task. The actionable insight: before asking for a better model, audit your product design, eval coverage, and user flows. Execution discipline beats model performance in most cases. 4. Your V1 AI product should solve a narrow, high-value problem with tight guardrails. Teams fail by trying to build a general-purpose assistant or agent on the first try. Pick one workflow, automate one repetitive task, or answer one category of question really well. Narrow scope lets you gather focused feedback, tune the model faster, and prove value before expanding. 5. Observability and logging are more critical for AI products than for traditional software, because AI behavior is non-deterministic and harder to debug. You should log not just errors but also model confidence scores, input characteristics, user corrections, and latency metrics. When something goes wrong in production, these logs are the only way to reconstruct what the model saw and why it made a particular decision. 6. Evals are necessary but not sufficient. Evals help you measure model performance on known test cases, but they don’t capture the full product experience, edge cases in production, or user satisfaction. Teams that rely solely on evals ship products that score well in testing but fail in the wild. Combine evals with continuous monitoring, user feedback loops, and observability tooling to catch what automated tests miss. 7. “Continuous calibration” replaces traditional iterative product development cycles. Because AI models drift and user expectations shift, teams must constantly measure real-world performance and adjust prompts, guardrails, or model versions. Without continuous calibration, your AI product will degrade silently, and users will churn before you notice.

AI Engineering has four levels to it! – Level 1: Using AI Start by mastering the fundamentals: -- Prompt engineering (zero-shot, few-shot, chain-of-thought) -- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face) -- Understanding tokens, context windows, and parameters (temperature, top-p) With just these basics, you can already solve real problems. – Level 2: Integrating AI Move from using AI to building with it: -- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus) -- Embeddings and similarity search (cosine, Euclidean, dot product) -- Caching and batching for cost and latency improvements -- Agents and tool use (safe function calling, API orchestration) This is the foundation of most modern AI products. – Level 3: Engineering AI Systems Level up from prototypes to production-ready systems: -- Fine-tuning vs instruction-tuning vs RLHF (know when each applies) -- Guardrails for safety and compliance (filters, validators, adversarial testing) -- Multi-model architectures (LLMs + smaller specialized models) -- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals) Here’s where you shift from “it works” to “it works reliably.” – Level 4: Optimizing AI at Scale Finally, learn how to run AI systems efficiently and responsibly: -- Distributed inference (vLLM, Ray Serve, Hugging Face TGI) -- Managing context length and memory (chunking, summarization, attention strategies) -- Balancing cost vs performance (open-source vs proprietary tradeoffs) -- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR) At this stage, you’re not just building AI—you’re designing systems that scale in the real world. What else would you add? Subscribe to my free blog for more learning blog.dataexpert.io

AI Engineering ≠ SW Engineering. Nor is it ML Engineering. Let’s stop the confusion once and for all. As an engineering manager, here’s what I see most engineers get wrong: not understanding what AI engineering truly looks like Let me give you solid, day-to-day examples: 1. Need a new feature? ⥽SWE: You scope out requirements, design a system, and write every line of logic yourself. ⥽AI Engineer: You find an existing AI model (say, GPT-5 or Gemini), and adapt it with prompts or lightweight fine-tuning to your use case. 2. When a business user asks, “Can we automate this?” ⥽SWE: You look for APIs, build custom rules, and code the workflow. ⥽AI Engineer: You ask, “Can an LLM or a vision model do 80% of this out-of-the-box?” If yes, you integrate, not re-invent. 3. Improving a search bar ⥽SWE: Optimize string matching, maybe build autocomplete from scratch. ⥽AI Engineer: Plug in embeddings from a pre-trained model for semantic search, no need to build new logic. 4. Document processing ⥽SWE: Regex, manual parsers, edge case handling. ⥽AI Engineer: Use an OCR + LLM pipeline, add guardrails to catch model hallucinations. 5. Product QA ⥽SWE: You test edge cases, business logic, inputs/outputs, and deterministic. ⥽AI Engineer: You test probabilistic outputs, run prompt variation tests, evaluate with real user data, and watch for bias/errors you can’t predict. 6. Release cycles ⥽SWE: Every change means a code update, deployment, and regression testing. ⥽AI Engineer: Sometimes, you just update a prompt or swap a model version, no full redeploy. 7. User feedback loop ⥽SWE: Feedback = bug report, fix the function, redeploy. ⥽AI Engineer: Feedback = adjust prompt, tweak the model, retrain, or even switch APIs. 8. Security ⥽SWE: Input sanitization, XSS/SQL injection checks, and access controls. ⥽AI Engineer: Prompt injection protection, controlling model responses, data redaction before sending to APIs. 9. Scaling ⥽SWE: Optimize backend, add load balancers, scale microservices. ⥽AI Engineer: Optimize model API usage, cache responses, batch queries to control token cost. 10. Hiring & skills ⥽SWE: Look for CS fundamentals, data structures, algorithms, OOP. ⥽AI Engineer: Look for prompt design, LLM adaptation, model evaluation, and rapid prototyping with AI APIs. Bottom line: → Software Engineers build logic from scratch. → ML Engineers train models from scratch. → AI Engineers build products with models already trained. The best combination is having solid fundamentals as a software engineer and then combining it with AI, so you can go beyond what it can do for you and give quality output.

Most people still think of LLMs as “just a model.” But if you’ve ever shipped one in production, you know it’s not that simple. Behind every performant LLM system, there’s a stack of decisions, about pretraining, fine-tuning, inference, evaluation, and application-specific tradeoffs. This diagram captures it well: LLMs aren’t one-dimensional. They’re systems. And each dimension introduces new failure points or optimization levers. Let’s break it down: 🧠 Pre-Training Start with modality. → Text-only models like LLaMA, UL2, PaLM have predictable inductive biases. → Multimodal ones like GPT-4, Gemini, and LaVIN introduce more complex token fusion, grounding challenges, and cross-modal alignment issues. Understanding the data diet matters just as much as parameter count. 🛠 Fine-Tuning This is where most teams underestimate complexity: → PEFT strategies like LoRA and Prefix Tuning help with parameter efficiency, but can behave differently under distribution shift. → Alignment techniques- RLHF, DPO, RAFT, aren’t interchangeable. They encode different human preference priors. → Quantization and pruning decisions will directly impact latency, memory usage, and downstream behavior. ⚡️ Efficiency Inference optimization is still underexplored. Techniques like dynamic prompt caching, paged attention, speculative decoding, and batch streaming make the difference between real-time and unusable. The infra layer is where GenAI products often break. 📏 Evaluation One benchmark doesn’t cut it. You need a full matrix: → NLG (summarization, completion), NLU (classification, reasoning), → alignment tests (honesty, helpfulness, safety), → dataset quality, and → cost breakdowns across training + inference + memory. Evaluation isn’t just a model task, it’s a systems-level concern. 🧾 Inference & Prompting Multi-turn prompts, CoT, ToT, ICL, all behave differently under different sampling strategies and context lengths. Prompting isn’t trivial anymore. It’s an orchestration layer in itself. Whether you’re building for legal, education, robotics, or finance, the “general-purpose” tag doesn’t hold. Every domain has its own retrieval, grounding, and reasoning constraints. ------- Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

The biggest myth in AI today? That tools like LLMs, CoPilots, MCPs, and Agents will do the engineering for you. They won’t — because AI is engineering. LLMs. MCP. Agents. They’re all just that — tools. Yet many organizations are spending an extraordinary amount of time comparing, evaluating, and switching between tools — while missing the real essence of AI transformation. The real differentiator isn’t the toolchain. It’s the engineering mindset behind how those tools are used. Most organizations miss that AI is an engineering discipline — not a collection of experiments. It demands the same rigor as any mature system: design, development, testing, validation, rollout, and continuous optimization. Don’t go by leaderboards — they’re tested to work in controlled benchmarks, not in real-world, multi-system environments where context, latency, data, and cost all collide. And don’t fall for the misconception that AI will replace engineers. That’s a narrative being set — but having worked with top LLMs and chatbots, one thing is clear: they often fail when confronted with real engineering. Their code lacks depth, structure, and holistic system thinking. Tools never replace real engineering. They amplify those who understand it. Invest in the core. Invest in robust engineering practices. Upskill your teams. This will be your foundation in building scalable, responsible, and future-ready AI systems. Because tools will change. Frameworks will evolve. But engineering excellence — that’s what endures #aiengineering #ai #leanagenticai

Kevin Klyman: "📣📣 We just published the third annual Foundation Model Transparency Index! Our comprehensive study shows that AI companies have become less transparent in 2025. Some highlights from the paper: ➡️ Transparency on the decline: The average transparency score for AI companies declined from 58/100 in 2024 to 40/100 in 2025. xAI scores lower than any company we have ever assessed, releasing almost no information about its practices or its flagship model. ➡️ Companies withhold key information: Top tech companies release little or no information about the environmental impact of AI, whose data they use to build their systems, or whether the risk mitigations they put in place actually work. We definitively show that this information is not publicly available and that companies refuse to release it. ➡️ Companies share the capabilities of their models, but do not adequately evaluate risks. Just 4 of 13 companies comprehensively evaluated risks prior to release of their foundation model and report results upon release, and only IBM releases an externally reproducible risk evaluation. ➡️ Companies have changed their practices to release less information. In 2024, Meta and Mistral released technical reports alongside their flagship models (Llama 2 and Mistral 7B), but in 2025 neither released technical reports (for Llama 4 and Mistral Medium 3 respectively). As a result, Meta no longer discloses which risk mitigations it uses, quantitative evaluations of those risk mitigations, the amount and type of hardware it used to train its model, or prohibited model behaviors. ➡️ Our method: We break down transparency of AI companies into 100 indicators, develop concrete definitions and rubrics for those indicators, and send each company a transparency report template to fill out. This year 7 companies filled out the transparency report, and we independently assessed 6 other companies. We then worked with these companies to help them improve their disclosures, often resulting in companies disclosing new information to the public. You can read the full paper in the comments below! Thanks to the team behind the index - Alex Wan, Sayash Kapoor, Nestor Maslej, Shayne Longpre, Betty Xiong, Percy Liang, Rishi Bommasani! I'd also like to thank Stanford Institute for Human-Centered Artificial Intelligence (HAI) for supporting this work, Loredana Fattorini for making the visuals, and the Foundation Model Transparency Index board for their guidance Dr. Rumman Chowdhury, Daniel Ho, Arvind Narayanan, Danielle Allen and Daron Acemoglu. "