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🧠 RAG systems are powerful — but they can get expensive fast.
Whether you’re optimizing inference pipelines or managing retrieval architecture, balancing cost and response quality is key to running a scalable production system. 💸 In this visual, I break down the primary cost drivers in Retrieval-Augmented Generation (RAG) systems: Here are a few strategies that have worked in practice:✅ Use smaller or quantized models, especially in…
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🚧 What makes production LLM systems challenging?
Deploying Retrieval-Augmented Generation (RAG) in the real world isn’t just about getting the model to work — it’s about making it work at scale, under pressure, and with messy data. Here are some of the toughest challenges I’ve encountered 👇 🧩 Challenges: ✅ Solutions: ⚖️ Trade-offs to navigate: 🔍 Evaluation strategies:Use code-based metrics, LLM-as-a-judge, and…
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💡 RAG vs. Fine-Tuning – Which One Should You Use?
When it comes to adapting LLMs, it’s not always RAG or fine-tuning — sometimes, the best choice is both. 🔍 RAG (Retrieval-Augmented Generation)Best for knowledge injection — perfect when your LLM needs access to current or external information.You can dynamically inject relevant context, and the model will use it effectively. 🎯 Fine-TuningBest for domain adaptation…
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🚀 Exploring Agentic RAG Systems + 4 Types of Agentic Workflows
Retrieval-Augmented Generation (RAG) gets a major upgrade when you add agentic workflows — modular, intelligent systems where LLMs collaborate like agents in a pipeline. Here’s a visual breakdown I created to showcase: 🔹 Agentic RAG SystemA router LLM determines whether to retrieve from the knowledge base. If yes, relevant documents are selected, evaluated, and turned…
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🚀 Mastering LLM Sampling Strategies
Whether you’re building creative tools or deploying factual assistants, understanding how language models generate text is key. Here’s a quick guide I created to demystify LLM sampling strategies — from temperature tuning to top-k/top-p decoding and token-specific tactics like repetition penalties and logit biases. 🔧 Tips to get started:1️⃣ Adjust temperature and top-p based on…
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🚀 Exploring LLM Observability Platforms: Why It Matters for RAG Systems
When working with Retrieval-Augmented Generation (RAG), visibility into each step of the pipeline is essential. That’s where LLM observability tools like Arize Phoenix come in. 🔍 What can Arize do? 📊 Why it’s valuable: 🔄 It’s a flywheel: Better observability → Better experiments → Better performance 🔧 Of course, no single tool solves everything. Tools…
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Understanding Transformer Architecture – Simplified!
Ever wondered how Large Language Models (LLMs) like GPT actually work under the hood? I created this visual guide to demystify the Transformer Architecture, the foundational design behind most modern language models. 🧠 At a high level: 💡 Fun fact: Some models have over 100 attention heads and 64+ layers, refining token embeddings with every…
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🔍 Three Semantic Search Architectures You Should Know 🔍
If you’re building or improving semantic search systems, understanding the three primary architectures—Bi-Encoder, Cross-Encoder, and ColBERT—is crucial. Each has trade-offs in quality, latency, storage, and use cases. Here’s a quick comparison: 1️⃣ Bi-Encoder✅ Fast and scalable✅ Great for production✅ Pre-computed document vectors🔻 Moderate quality compared to others 2️⃣ Cross-Encoder✅ Best quality—contextual understanding of prompt +…
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🚀 Query Parsing in LLM-Powered Retrieval Systems
Created this visual to break down 3 key techniques we can use to improve search relevance and retrieval quality: 1️⃣ Query RewritingUse an LLM to rewrite messy or ambiguous prompts into optimized ones before passing them to the retriever. This improves the likelihood of relevant results. 2️⃣ Named Entity Recognition (NER)Extract specific entities (e.g. names,…
