Real-world AI, explained simply — with code, use cases, and zero fluff.
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PODCAST — GraphRAG vs. Vector RAG: Better Retrieval or Expensive Overengineering?
Prefer reading instead? The full article is available here (https://aipractitioner.substack.com/p/graphrag-vs-vector-rag-better-retrieval). The podcast is also available on Spotify (https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX) and Apple Podcasts (https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899). Subscribe to keep up with the latest drops.
Standard RAG works well when the answer is contained in one place. But when a question requires connecting facts across several documents, vector retrieval can start to struggle.
GraphRAG adds explicit structure around entities and their relationships, making multi-hop retrieval easier, but also more complex and expensive.
In this episode, the focus is on how GraphRAG works, how the main approaches differ, and whether the added complexity is actually worth it.
You’ll learn:
* Why does vector retrieval struggle with multi-hop questions? Exploring why semantic similarity alone is often not enough when an answer requires connecting several entities and facts across documents.
* How do the main GraphRAG architectures differ? Breaking down community-based approaches like Microsoft GraphRAG, entity-relation systems like LightRAG, PageRank-based retrieval such as HippoRAG, and recursive summarization with RAPTOR.
* Is GraphRAG actually worth the additional cost? Looking at benchmark results, retrieval quality, token usage, latency, and practical implementations of NaiveRAG, LightRAG, and Microsoft GraphRAG.
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9 Sept 2026
PODCAST — Inside TabFM: How In-Context Learning Enables Prediction on Unseen Tables
Prefer reading instead? The full article is available here (http://aipractitioner.substack.com). The podcast is also available on Spotify (https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX) and Apple Podcasts (https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899). Subscribe to keep up with the latest drops.
Most tabular machine learning workflows start the same way: clean the data, engineer features, tune hyperparameters, and train a new model from scratch.
Google Research’s TabFM proposes a very different approach: treat the dataset itself as context and make predictions in a single forward pass, without task-specific training.
In this episode, the focus is on how zero-shot foundation models for tabular data actually work, why tables require a different architecture from text, and how TabFM compares with the models that came before it.
You’ll learn:
* Why is in-context learning harder for tabular data? Exploring how permutation invariance across rows and columns makes standard sequence modeling a poor fit for tables.
* How do TabPFN, TabICL, and TabFM solve the problem differently? Breaking down three architectural approaches to the same challenge and the key ideas that distinguish them.
* How do you run these models in practice? Using TabFM, TabPFN, and TabICL on real tabular datasets and looking at the kind of predictive performance you can expect from them.
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2 Sept 2026
PODCAST — LLM Cost Prediction: From Single Prompts to Agentic Systems
Prefer reading instead? The full article is available here (https://aipractitioner.substack.com/p/llm-cost-prediction-from-single-prompts?r=49ttp&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true). The podcast is also available on Spotify (https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX) and Apple Podcasts (https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899). Subscribe to keep up with the latest drops.
LLM costs are easy to calculate after execution, but estimating them before a prompt or agent actually runs is a much harder problem.
In this episode, the focus is on predicting LLM and agent costs before execution, from estimating output length for a single model call to reasoning about the much more variable execution paths of AI agents.
You’ll learn:
* What can be predicted from a prompt before execution? Exploring how token consumption, output length, and model cost can be estimated before sending a request to an LLM.
* Can an agent’s cost be estimated before execution? Understanding the additional uncertainty introduced by multiple model calls, tool calls, retries, reasoning steps, and variable execution trajectories.
* What Python tooling is available today? Reviewing practical libraries and approaches for estimating and calculating LLM and agent costs, including LiteLLM, genai-prices, tokencost, and tiktoken.
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