The Complete RAG Playbook (Part 4): Evaluation & Choosing What Works

The Complete RAG Playbook (Part 4): Evaluation & Choosing What Works

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Author(s): Ravi Kumar Verma Originally published on Towards AI. The Complete RAG Playbook (Part 4): Evaluation & Choosing What Works We’ve covered 19 RAG techniques across three parts. You’ve seen chunking strategies, context enrichment, query transforms, rerankers, and advanced architectures. But there’s one question nobody likes to answer: Image by author using GeminiThis article focuses on the evaluation of RAG techniques, outlining the importance of measurement to determine which implementation works best for specific use cases. The author […]

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Learning Bayesian and Markov Networks with an Unreliable Oracle

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We study constraint-based structure learning of Markov networks and Bayesian networks in the presence of an unreliable conditional independence oracle that makes at most a bounded number of errors. For Markov networks, we observe that a low maximum number of vertex-wise disjoint paths implies that the structure is uniquely identifiable even if the number of errors is (moderately) exponential in the number of vertices. For Bayesian networks, however, we prove that one cannot tolerate any errors to always […]

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Any to Full: Prompting Depth Anything for Depth Completion in One Stage

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arXiv:2603.05711v1 Announce Type: new Abstract: Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to introduce domain-general geometric priors, but current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional […]

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Where Do Flow Semantics Reside? A Protocol-Native Tabular Pretraining Paradigm for Encrypted Traffic Classification

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arXiv:2603.10051v1 Announce Type: new Abstract: Self-supervised masked modeling shows promise for encrypted traffic classification by masking and reconstructing raw bytes. Yet recent work reveals these methods fail to reduce reliance on labeled data despite costly pretraining: under frozen encoder evaluation, accuracy drops from greater than 0.9 to less than 0.47. We argue the root cause is inductive bias mismatch: flattening traffic into byte sequences destroys protocol-defined semantics. We identify three specific issues: 1) field unpredictability, random fields like […]

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Multi-Integration of Labels across Categories for Component Identification (MILCCI)

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arXiv:2602.04270v1 Announce Type: cross Abstract: Many fields collect large-scale temporal data through repeated measurements (trials), where each trial is labeled with a set of metadata variables spanning several categories. For example, a trial in a neuroscience study may be linked to a value from category (a): task difficulty, and category (b): animal choice. A critical challenge in time-series analysis is to understand how these labels are encoded within the multi-trial observations, and disentangle the distinct effect of each […]

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Deep Learning in NLP

Deep Learning in NLP

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This post is an old debt. Since I’ve started this blog 3 years ago, I’ve been refraining from writing about deep learning (DL), with the exception of occasionally discussing a method that uses it, without going into details. It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level. But perhaps worse than that, DL somewhat contradicts the description of my blog: “human-interpretable computer science”. We don’t really […]

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My journey to the microwave alternate timeline

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Published on February 10, 2026 5:59 PM GMT Cross-posted from Telescopic Turnip Recommended soundtrack for this post As we all know, the march of technological progress is best summarized by this meme from Linkedin: Inventors constantly come up with exciting new inventions, each of them with the potential to change everything forever. But only a fraction of these ever establish themselves as a persistent part of civilization, and the rest vanish from collective consciousness. Before shutting down forever, […]

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Why My AI Rollout Failed Until I Designed With Teams, Not For Them

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The failure wasn’t technical When you’re responsible for rolling out AI inside a company, it’s easy to focus on the visible parts. Models. Connectors. Retrieval. Permissions. Latency. That’s the work everyone sees. But that’s not why my rollout struggled. It didn’t fail because the system was broken. It failed because I was designing AI for teams instead of with them. At Nextdoor, our early Work AI rollout stalled even though the technology was solid. Accuracy was fine. Performance […]

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Designing a Multi-Seller Platform With Stripe Connect Express

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Overview We have recently built and launched our multi-seller SaaS B2B platform with Stripe Connect. Our platform is intended for small businesses with mainly offline presence to easily start selling gift cards online. Stripe Connect Express makes it possible to build a multi-seller platform surprisingly quickly. You can onboard sellers, take application fees, and route payments directly to third-party accounts with relatively little code. Stripe can handle compliance, payouts, and much of the operational complexity. However, in practice, […]

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Semantic Chunking vs Fixed Chunking: Why Your RAG’s Retrieval Quality Starts Before the Query

Semantic Chunking vs Fixed Chunking: Why Your RAG’s Retrieval Quality Starts Before the Query

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This is Part 2 of a 5-part series on building a production-grade RAG system. Part 1 — Hybrid RAG Overview Part 2 — Semantic Chunking vs Fixed Chunking (you are here) Part 3 — Custom Reranking: Combining Dense and Sparse Scores Part 4 — Parent-Child Document Architecture Part 5 — Building a Tool-Augmented RAG Agent Most RAG failures are not retrieval failures — they’re chunking failures. The vector database returns exactly what you put in. If you split documents at arbitrary character boundaries, you will embed incomplete thoughts, cut off mid-sentence, and force your model […]

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