Integrating AI In Engineering Solutions

The GenAI wave is real, but most engineers still feel stuck between hype and practical skills. That’s why I created this 15-step roadmap—a clear, technically grounded path to transitioning from traditional software development to advanced AI engineering. This isn’t a list of buzzwords. It’s the architecture of skills required to build agentic AI systems, production-grade LLM apps, and scalable pipelines in 2025. Here’s what this journey actually looks like: 🔹 Foundation Phase (Steps 1–5): → Start with Python + libraries (NumPy, Pandas, etc.) → Brush up on data structures & Big-O — still essential for model efficiency → Learn basic math for AI (linear algebra, stats, calculus) → Understand the evolution of AI from rule-based to supervised to agentic systems → Dive into prompt engineering: zero-shot, CoT, and templates with LangChain 🔹 Build & Integrate (Steps 6–10): → Work with LLM APIs (OpenAI, Claude, Gemini) and use function calling → Learn RAG: embeddings, vector DBs, LangChain chains → Build agentic workflows with LangGraph, CrewAI, and AutoGen → Understand transformer internals (positional encoding, masking, BERT to LLaMA) → Master deployment with FastAPI, Docker, Flask, and Streamlit 🔹 Production-Ready (Steps 11–15): → Learn MLOps: versioning, CI/CD, tracking with MLflow & DVC → Optimize for real workloads using quantization, batching, and distillation (ONNX, Triton) → Secure AI systems against injection, abuse, and hallucination → Monitor LLM usage and performance → Architect multi-agent systems with state control and memory Too many “AI tutorials” skip the real-world complexity, including permissioning, security, memory, token limits, and agent orchestration. But that’s what actually separates a prototype from a production-grade AI app. If you’re serious about becoming an AI Engineer, this is your blueprint. And yes, you can start today. You just need a structured plan and consistency. Feel free to save, share, or tag someone on this journey.

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

I've come to the realization that the most underrated skill for building with AI (and arguably the one that will separate high-output teams from everyone else) is task decomposition. Not vibe coding. Not prompt engineering. Decomposition. If you can’t break a goal down into clear, sequenced tasks, you can’t: - Tell where AI can help - Assign work to the right tools or people - Or build a system that compounds instead of collapses Most people try to “delegate to AI” before they’ve even defined the work. And here’s the non-obvious part: When you decompose a task well, you don’t just make AI useful, you create a blueprint that makes your entire org more intelligent. Your workflows get clearer. Your automation paths become visible. You uncover handoffs and decisions that were implicit before — now they can be improved, delegated, measured. Take a real example Let’s say your goal is: "Create an email campaign for churned customers." Break it down like this: - Define what "churned" means and who qualifies (Data task) - Analyze why those customers left (Behavioral analysis) - Decide what message or offer might bring them back (Strategy) - Write subject lines and body copy (Creative) - Design and QA the email (Design & QA) - Set up the send and monitor results (Execution & Analytics) Every line above is a chance for AI to plug in but only after the thinking is done. For product managers, this is especially critical. The best PMs won’t just focus on vibes — they’ll design the workflows that give AI a role in real-world systems. They’ll decompose user intent, structure execution, and orchestrate tools and agents like a director, not just an architect. And this is the deeper truth: AI doesn’t make teams obsolete — it makes shallow thinkers obsolete. The future belongs to people and products that know how to break things down and build from the pieces — thoughtfully, repeatedly, and at scale. Get great at task decomposition. It’s the new core skill of the AI era.

I built an entire PCB from scratch in 35 minutes using AI. FLUX.AI COPILOT transformed my engineering workflow. Starting point: A student's basic block diagram End result: Complete schematic + 3D layout The AI asked intelligent questions: "Which USB serial IC - CH340G or FT232RL?" "Internal or external oscillator?" "Do you want an RC filter on your inputs?" Real engineering decisions while AI handled the grunt work. Technical breakdown: POWER SUPPLY • Generated optimal rail voltages • Added protection circuitry • Selected efficient regulators MICROCONTROLLER • Automated pin assignments • Optimized peripheral routing • Generated decoupling network COMMUNICATION • USB serial interface • I2C expansion ports • Debug headers placement SENSORS • Light-dependent resistors • Temperature monitoring • Motion detection The most powerful part for me were these: AI suggested improvements I wouldn't consider if I were a beginner or even intermediate, but are common for advanced design: • Better ground plane distribution • Reduced EMI through strategic routing • Thermal optimization via component placement This tool cuts my design time by 80%. Engineering evolves. Tools improve. We adapt or fall behind. I've documented the entire process in a free roadmap video. I'll share it with anyone who comments below. Serious about accelerating your PCB design workflow? Drop "Flux" in the comments. Like this post if you believe AI assistants will revolutionize hardware design - or at least make it A LOT easier, faster and more accurate.

It’s true that AI and GenAI are raising the bar for data quality and transforming the entire software engineering landscape. This evolution helps pave the way for the next wave of applications (like Agentic AI) and unlocking GenAI’s full potential.     Recently, my Deloitte colleagues (Ashish Verma, Prakul Sharma, Parth Patwari, Alfons Buxó, Diana Kearns-Manolatos (she/her), and Ahmed Alibage, CMS®, Ph.D.) identified four crucial engineering challenges that leaders need to address to enhance data and model quality:     1. Data strategy and architecture. A clear data architecture that considers diversity and bias is essential for any GenAI strategy to succeed.    2. Probabilistic models.  Traditional systems fall short for GenAI, which thrives on probabilistic models with tools like vector databases and knowledge graphs.    3. Data integration and engineering. Retrieval augmented generation (RAG) and multi-modal approaches bring integration challenges; solutions include automated quality reviews and better chunking and retrieval methods.    4. Model opacity and hallucinations. GenAI models can occasionally hallucinate, which impacts trust. Human oversight and advanced machine learning techniques can help detect and correct inaccuracies.     Highly encourage a read into these fascinating solutions to maintain software quality and build trust: (https://deloi.tt/42RqlHs).   

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

Thought provoking and great conversation between Aravind Srinivas (Founder, Perplexity) and Ali Ghodsi (CEO, Databricks) today Perplexity Business Fellowship session sometime back offering deep insights into the practical realities and challenges of AI adoption in enterprises. TL;DR: 1. Reliability is crucial but challenging: Enterprises demand consistent, predictable results. Despite impressive model advancements, ensuring reliable outcomes at scale remains a significant hurdle. 2. Semantic ambiguity in enterprise Data: Ali pointed out that understanding enterprise data—often riddled with ambiguous terms (C meaning calcutta or california etc.)—is a substantial ongoing challenge, necessitating extensive human oversight to resolve. 3. Synthetic data & customized benchmarks: Given limited proprietary data, using synthetic data generation and custom benchmarks to enhance AI reliability is key. Yet, creating these benchmarks accurately remains complex and resource-intensive. 4. Strategic AI limitations: Ali expressed skepticism about AI’s current capability to automate high-level strategic tasks like CEO decision-making due to their complexity and nuanced human judgment required. 5. Incremental productivity, not fundamental transformation: AI significantly enhances productivity in straightforward tasks (HR, sales, finance) but struggles to transform complex, collaborative activities such as aligning product strategies and managing roadmap priorities. 6. Model fatigue and inference-time compute: Despite rapid model improvements, Ali highlighted the phenomenon of "model fatigue," where incremental model updates are becoming less impactful in perception, despite real underlying progress. 7. Human-centric coordination still essential: Even at Databricks, AI hasn’t yet addressed core challenges around human collaboration, politics, and organizational alignment. Human intuition, consensus-building, and negotiation remain central. Overall the key challenges for enterprises as highlighted by Ali are: - Quality and reliability of data - Evals- yardsticks where we can determine the system is working well. We still need best evals. - Extreme high quality data is a challenge (in that domain for that specific use case)- Synthetic data + evals are key. The path forward with AI is filled with potential—but clearly, it's still a journey with many practical challenges to navigate.

  AI is a game-changer for sustainability at work. At Amazon, our culture is rooted in innovation and speed. AI can enable both, and we’re using it in ways big and small to make progress. Here are just a few examples: 📦 The Package Decision Engine - we created this AI model to make sure items arrive on your doorstep safely, in the most efficient packaging possible. It makes decisions using deep machine learning, natural language processing and computer vision. What does this mean for sustainability? So far, along with other packaging innovations, the Package Decision Engine has helped us avoid over 2-million tons of packaging material worldwide. 🏢 AI Tools for Buildings - You may be surprised to hear that buildings and their construction account for 40% of the world's greenhouse gas emissions. We’re using a suite of AI tools to help manage energy and water use in more than 100 of our buildings. One example: a tool built by Amazon Web Services (AWS) called FlowMS led engineers at a logistics facility to an underground leak, and fixing it helped prevent the loss of over 9-million gallons of water per year. Other AI tools help us monitor our HVAC systems, refrigeration units, and dock doors. These seemingly simple solutions add up, and we're making meaningful progress in saving energy. 🤖 Maximo - Arguably one of the coolest-looking examples, Maximo is an AI-powered robot developed by The AES Corporation helping build solar farms, including projects backed by Amazon. It uses computer vision to lift heavy panels, makes decisions with real-time construction intelligence, and helps construction crews avoid dangerous heat. All told, Maximo can reduce solar construction timelines and costs by as much as 50%. This is just the beginning, and I’m excited about all the ways AI can help us reach our goals. If you’d like to dive deeper into how we’re using it in our buildings, you’ll find more details here: https://lnkd.in/gU_UmWbq

Researchers have unveiled a self-harmonized Chain-of-Thought (CoT) prompting method that significantly improves LLMs’ reasoning capabilities. This method is called ECHO. ECHO introduces an adaptive and iterative refinement process that dynamically enhances reasoning chains. It starts by clustering questions based on semantic similarity, selecting a representative question from each group, and generating a reasoning chain using zero-shot CoT prompting. The real magic happens in the iterative process: one chain is regenerated at random while others are used as examples to guide the improvement. This cross-pollination of reasoning patterns helps fill gaps and eliminate errors over multiple iterations. Compared to existing baselines like Auto-CoT, this new approach yields a +2.8% performance boost in arithmetic, commonsense, and symbolic reasoning tasks. It refines reasoning by harmonizing diverse demonstrations into consistent, accurate patterns and continuously fine-tunes them to improve coherence and effectiveness. For AI engineers working at an enterprise, implementing ECHO can enhance the performance of your LLM-powered applications. Start by training your model to identify clusters of similar questions or tasks in your specific domain. Then, implement zero-shot CoT prompting for each representative task, and leverage ECHO’s iterative refinement technique to continually improve accuracy and reduce errors. This innovation paves the way for more reliable and efficient LLM reasoning frameworks, reducing the need for manual intervention. Could this be the future of automatic reasoning in AI systems? Paper https://lnkd.in/gAKJ9at4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai