How to Use AI Tools in Software Engineering

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

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.

"Function calling isn’t working." "My Search tool is broken." "The agent isn't doing what I expect with BigQuery." Sound familiar? When a tool fails in an AI agent, the instinct is often to blame the framework 😁 And while we love (!) the feedback, as I get into the weeds with customers, we often find the issue hiding somewhere else. So it becomes important to start seeing the agent and its tools as a layer cake and apply classic software engineering discipline: isolate the failure by debugging layer by layer. Here’s the 4-layer framework for debugging tool-use with agents, and how to use adk web to do it: 1️⃣ The Tool Layer: Does your tool's code work in isolation? Before you even look at a trace, run your function with a hardcoded input. If it fails here, it's a bug in your tool's logic. 2️⃣ The Model Layer: Is the LLM generating the correct intent? This is where traces are invaluable. In adk web, look at the trace for the step right before the tool call. You can see the exact prompt sent to the model and the raw LLM output. Is the model choosing the right tool? Are the parameters plausible? If not, the issue is your prompt or tool description. 3️⃣ The Connection Layer: This is where the model's request meets your code. Is there a mismatch? Use adk web to check the exact arguments the LLM tried to pass to your function. Are the parameter names correct? Is a number being passed as a string? The trace makes it obvious if the LLM's understanding doesn't match your function's signature. 4️⃣ The Framework Layer: If the first three layers look good, now we look at the orchestration. How did the agent handle the tool's output? Use adk web to check the full trace is the story of your agent's execution. You can see the data returned by the tool and the subsequent LLM call where the agent decides what to do next. This is where you'll spot issues in your agent's logic flow. This methodical approach, powered by observability tools like traces, turns a vague "my agent is broken" into a more precise diagnosis. How do you debug your agents tool-use? Comment below if a deep dive into any of these area would be useful! #AI #Agents #Gemini #DeveloperTools #FunctionCalling #Debugging #Observability

Check out this framework for building AI Agents that work in production. There are many recommendations out there, so would like your feedback on this one. This is beyond picking a fancy model or plugging in an API. To build a reliable AI agent, you need a well-structured, end-to-end system with safety, memory, and reasoning at its core. Here’s the breakdown: 1.🔸Define the Purpose & KPIs Start with clarity. What tasks should the agent handle? Align goals with KPIs like accuracy, cost, and latency. 2.🔸Choose the Right Tech Stack Pick your tools: language, LLM, frameworks, and databases. Secure secrets early and plan for production-readiness from day one. 3.🔸Project Setup & Dev Practices Structure repos for modularity. Add version control, test cases, code linting, and cost-efficient development practices. 4.🔸Integrate Data Sources & APIs Link your agent with whatever data it needs to take action intelligently from PDFs, Notion, databases, or business tools. 5.🔸Build Memory & RAG Index knowledge and implement semantic search. Let your agent recall facts, documents, and links with citation-first answers. 6.🔸Tools, Reasoning & Control Loops Empower the agent with tools and decision-making logic. Include retries, validations, and feedback-based learning. 7.🔸Safety, Governance & Policies Filter harmful outputs, monitor for sensitive data, and build an escalation path for edge cases and PII risks. 8.🔸Evaluate, Monitor & Improve Use golden test sets and real user data to monitor performance, track regressions, and improve accuracy over time. 9.🔸Deploy, Scale & Operate Containerize, canary-test, and track usage. Monitor cost, performance, and reliability as your agent scales in production. Real AI agents are engineered step by step. Hope this guide gives you the needed blueprint to build with confidence. #AIAgents

I've been using AI coding tools for a while now & it feels like every 3 months the paradigm shifts. Anyone remember putting "You are an elite software engineer..." at the beginning of your prompts or manually providing context? The latest paradigm is Agent Driven Development & here are some tips that have helped me get good at taming LLMs to generate high quality code. 1. Clear & focused prompting ❌ "Add some animations to make the UI super sleek" ✅ "Add smooth fade-in & fade out animations to the modal dialog using the motion library" Regardless of what you ask, the LLM will try to be helpful. The less it has to infer, the better your result will be. 2. Keep it simple stupid ❌ Add a new page to manage user settings, also replace the footer menu from the bottom of the page to the sidebar, right now endless scrolling is making it unreachable & also ensure the mobile view works, right now there is weird overlap ✅ Add a new page to manage user settings, ensure only editable settings can be changed. Trying to have the LLM do too many things at once is a recipe for bad code generation. One-shotting multiple tasks has a higher chance of introducing bad code. 3. Don't argue ❌ No, that's not what I wanted, I need it to use the std library, not this random package, this is the 4th time you've failed me! ✅ Instead of using package xyz, can you recreate the functionality using the standard library When the LLM fails to provide high quality code, the problem is most likely the prompt. If the initial prompt is not good, follow on prompts will just make a bigger mess. I will usually allow one follow up to try to get back on track & if it's still off base, I will undo all the changes & start over. It may seem counterintuitive, but it will save you a ton of time overall. 4. Embrace agentic coding AI coding assistants have a ton of access to different tools, can do a ton of reasoning on their own, & don't require nearly as much hand holding. You may feel like a babysitter instead of a programmer. Your role as a dev becomes much more fun when you can focus on the bigger picture and let the AI take the reigns writing the code. 5. Verify With this new ADD paradigm, a single prompt may result in many files being edited. Verify that the code generated is what you actually want. Many AI tools will now auto run tests to ensure that the code they generated is good. 6. Send options, thx I had a boss that would always ask for multiple options & often email saying "send options, thx". With agentic coding, it's easy to ask for multiple implementations of the same feature. Whether it's UI or data models asking for a 2nd or 10th opinion can spark new ideas on how to tackle the task at hand & a opportunity to learn. 7. Have fun I love coding, been doing it since I was 10. I've done OOP & functional programming, SQL & NoSQL, PHP, Go, Rust & I've never had more fun or been more creative than coding with AI. Coding is evolving, have fun & let's ship some crazy stuff!