Building a RAG System from Scratch: Retrieval Challenges

Why most RAG systems fail in the first week... It’s rarely the LLM's fault. Usually, the "Retrieval" part of RAG is broken. If you’re seeing poor results, check these three specific areas from the infographic: Chunking Strategy: Are you splitting documents effectively or cutting sentences in half? Re-ranking: Are you just taking the top 5 vector results, or are you validating their relevance before passing them to the LLM? Data Processing: Garbage in, garbage out. Are you cleaning your data before indexing? Building a Production-Ready RAG Pipeline requires a holistic view of the data lifecycle. (Great visual breakdown by QuantumEdgeX!) What’s your "must-have" component for a reliable AI agent? #SoftwareEngineering #ArtificialIntelligence #Python #VectorDatabase #RAGPipeline

🚀 Learning update: Non-negative Matrix Factorization(NMF) Today I explored something really interesting… A model that doesn’t just reduce data, but actually explains it. 🧩 What is NMF? Non-negative Matrix Factorization, like PCA, but more interpretable. 🔍 How It Works It breaks data into parts: - Components → patterns/themes - Features → how much of each pattern exists 🔄 Reconstruction Idea Original data ≈ combination of learned parts That’s the “factorization” part. 🎯 Real-World Use: Recommender Systems - Convert articles → topic features - Compare using cosine similarity - Recommend similar content 💡 Key Insight Even if two articles look different, if they share topics, they’re similar. 🔥 My Takeaway This is where ML starts to feel real, From patterns → to actual applications Definitely one of my favorite concepts so far. #MachineLearning #NMF #RecommenderSystems #DataScience #Python #DataCamp #DataCampAfrica

I tested GPT-4.1-mini vs Claude 3.5 Sonnet on SEC 10-Q filings using a custom Python benchmarking framework. What I expected: → Differences in reasoning quality What I found: → The biggest performance gap came from how each model extracted the data Not analysis. Not math. Input fidelity. This is a big deal for anyone building: • AI-driven financial reporting • Portfolio benchmarking tools • Automated KPI systems Because if your extraction is off, everything downstream is noise. Garbage in → confident garbage out. #ArtificialIntelligence #GenerativeAI #AIInFinance #DataStrategy #BusinessIntelligence #Python #DataScience #PrivateEquity #PortfolioPerformance #ValueCreation #DigitalTransformation #FinTech #LLMEvaluation #Automation

My market analysis engine runs 17 phases every week. 12 of them are deterministic Python. They finish in 15 seconds. The other 5 involve AI narratives, web searches, and editorial synthesis. They take 35 minutes. The critical insight: the analytical foundation — regime classification, volatility forecasting, tail-risk adjustment, sector dispersion — is locked in before the AI ever touches it. Here's what that means for the numbers you see in my market research: When the engine says "regime shift probability is 47%," I can trace it through the exact computation. The skewness input (-0.43), the kurtosis input (1.11), the Cornish-Fisher formula, the adjusted probability. No black box. No "in my experience." Just auditable math. Part 2 of my framework series drops tomorrow — inside the US equity engine. Have you ever traced a probability back to its actual computation? #QuantFinance #Python #MarketAnalysis #SystematicTrading #Volatility #HMM

Today I solved the Rotate Function problem and it was a great reminder of how powerful mathematical thinking can be in problem-solving. My first thought was straightforward: Rotate the array each time and calculate the function value. But that approach costs O(n²). Then came the real insight: Instead of recomputing every rotation, I derived a relationship between the current rotation and the next one. That single observation reduced the solution to: ✅ O(n) time ✅ O(1) extra space What I learned: Not every optimization comes from advanced data structures or complex algorithms. Sometimes, the biggest improvement comes from asking: “What changes between one step and the next?” That question can turn repeated work into reusable work. Small problem. Big lesson. Consistently learning, improving, and sharpening problem-solving skills—one problem at a time. #DataStructures #Algorithms #Python #LeetCode #ProblemSolving #CodingJourney #SoftwareEngineering

The Art of Focus: Mastering Image Cropping with NumPy! 🎯✂️ Day 86/100 In a world of data noise, the ability to focus on what matters is a superpower. For Day 86 of my #100DaysOfCode journey, I explored Region of Interest (ROI) Extraction. In Computer Vision, we don't always need the full picture. By using NumPy Array Slicing, I can 'zoom in' on specific coordinates to isolate faces, text, or objects for further analysis. Technical Highlights: 🎯 ROI Identification: Mastering the coordinate system to pinpoint and extract sub-matrices from large image arrays. ✂️ Precision Slicing: Leveraging Python's [start:stop] syntax to perform lossless cropping in microseconds. ⚡ Computational Optimization: Learning why reducing image size via cropping is the first step in high-speed object detection. 🤖 AI Preprocessing: Understanding how cropping helps prepare datasets for deep learning models by removing irrelevant background noise. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #100DaysOfCode #ComputerVision #NumPy #Python #BTech #IILM #AIML #ImageProcessing #DataScience #SoftwareEngineering #LearningInPublic #WomenInTech

So, you built your first AI agent in 5 minutes. Now what? 🤔 An LLM on its own is smart, but an agent without tools is locked in a box. To make it useful, you need to connect it to your data. Using Python and the Google ADK, implementing tool-calling is incredibly simple. Want your agent to analyze commodity prices? 🐍 Write a simple Python function that queries your time-series database. 🧠 Hand that function to your agent. You no longer just have an LLM—you have a data engineering assistant that can pull live metrics, run the math, and summarize the trends for you on command. Do you want to learn how? Let me know below! 👇 #ArtificialIntelligence #GoogleADK #PythonDeveloper #DataScience #SkynetAcademy

Did you know you can build your own Senior Data Analyst using just Python, Gemini, and the Google ADK? 🤖📊 LLMs only become true agents when they can interact with the real world. By giving them access to custom tools, they can securely connect to your databases, run complex queries, and analyze trends—just like a human engineer would. Stop just chatting with AI, and start putting it to work. Learn how to set up tool-calling right here 👇 #LLMs #DataScience #DataAnalytics #Gemini #SkynetAcademy

 EDA & Feature Engineering 📊 Garbage in = Garbage out. That's why EDA comes first. Before you touch any ML model, you need to understand your data. EDA = Exploratory Data Analysis ✅ Check shape, types, nulls ✅ Plot distributions is it skewed? ✅ Find correlations -heatmaps reveal hidden patterns ✅ Spot outliers before they ruin your model Then comes Feature Engineering — turning raw columns into model fuel: → Encoding categories → Scaling numbers → Creating new features → Dropping irrelevant ones I'm learning this at Humber College, Ontario while building real ML projects 🎓 What's your go-to EDA library -- Pandas Profiling, Seaborn, or Plotly? #EDA #FeatureEngineering #MachineLearning #DataScience #Python #HumberCollege

Predict categories, not numbers. 3 classification models. One free notebook. This notebook covers: → Logistic Regression — the baseline every ML project needs → Decision Trees — visual, interpretable, easy to explain to stakeholders → K-Nearest Neighbors — surprisingly powerful for small datasets → Train/test split and why it matters → Confusion matrix: true positives, false positives, and why accuracy lies → Precision vs Recall — when each one matters more → Model comparison on the same dataset Every model is trained, evaluated, and compared. Not theory slides. Runnable code with real output. If you're prepping for ML interviews, this is the notebook to start with. Free: https://lnkd.in/gCNvPJqS Day 2/7. Yesterday was Web Scraping. Tomorrow: APIs. #MachineLearning #Classification #Python #DataScience #DecisionTree #LogisticRegression #InterviewPrep #FreeResources