RAG
166 episodes
#6520: Mapping Multi-Tenancy Across Vector Databases
Isolating tenants physically into namespaces can cut vector query costs a hundredfold compared to using metadata filters on shared indexes.
#6479: Why Research Agent Agreement Is Not Independence
When two distinct language models fail a task, they give the exact same wrong answer sixty percent of the time.
#6462: When Adding a Reranker Hurts Search
NVIDIA benchmarked a cross-encoder upgrade that scored three points below its baseline, while other models surged fifteen.
#6398: Why Named Entity Linking Fails After the Span Is Found
Zero-shot large language models still lag behind supervised systems on named entity recognition benchmarks by over eleven F1 points.
#6390: Why Editing Vector Databases Silently Re-Embeds Text
A one-word edit cannot nudge existing vector numbers; the model must re-embed the chunk from scratch or search answers with deleted text.
#6389: Decaying Old Memories in Vector Search
A time-decay multiplier can drop a 0.92 similarity result down to 0.41, allowing mediocre fresh matches to overtake it.
#6387: What Really Runs When You Compact an Agent's Context
Commercial compaction relies on main models and prompts, while purpose-built fine-tunes like Compactor-Qwen3.5-4B remain rare.
#6370: Why Modern Audio Datasets Ship as Parquet Files
Parquet files store metadata at the end so clients can query durations or transcripts via HTTP range requests without downloading audio bytes.
#6310: Why Model APIs Lack Dedicated Few-Shot Fields
Zou and colleagues found models suffered steep drops at 16,000 tokens on tasks requiring learning across an entire demonstration set.
#6307: Why Exact Matching Outlasts the Index
Ken Thompson extracted grep from the ed editor in 1973 to search the Federalist Papers without loading entire files into memory.
#6306: Training Custom Rerankers for Podcast Search
Expanding a retrieval candidate pool past 300 items yielded less than a single percentage point of recall in legal benchmark evaluations.
#6304: Why Semantic Desktop Search Struggles to See Files
Local vector indexing on macOS and Windows requires hours of idle, plugged-in time to parse drives, often with no progress bar.
#6286: Why Claude Code Replaced Vector Search with Grep
Claude Code creator Boris Cherny revealed early prototypes used local vector search before abandoning it for agentic grep.
#6271: Why Video Search Often Works Better as Text
A one-minute video compressed into a single vector undergoes roughly five orders of magnitude of raw data reduction.
#6265: Why Upgrading Embedding Models Demands Reindexing
Cohere's Embed Five Fast queries against a Pro index reach 98.4 percent of performance without regenerating stored document vectors.
#6264: Choosing Omni Embedding Models for Mixed Media
Jina's omni model trains only 0.35 percent of its weights while aligning with existing text indices.
#6263: Why Numeric Data Lacks a Default Embedding Model
Popular time-series models like IBM's Granite TSPulse forecast trends rather than provide canonical vectors for similarity search.
#6146: Grounding Is Not Solved: DIY vs Gateway vs Native Search
Bolt-on search, gateway search, or native grounding? The plumbing didn't vanish — it just moved to someone else's bill.
#6110: The Chatbot OpenAI Almost Shelved
Instruction models existed for months. Then a rumour about Anthropic pushed OpenAI to ship a chat box in two weeks.
#6087: Building a Podcast Graph That Doesn't Suck
Daniel wants to turn a 6,000-episode archive into a walkable graph. Turns out the hard part isn't the database — it's the pruning.
#5950: Self-Hosting Your Search Stack: SearXNG, Crawl4AI, and the Real Bill
Can SearXNG, Crawl4AI, and an open-source aggregator really replace a commercial search API? Yes — with a very large asterisk.
#5949: Inside Deep Research Agents: Claims, Citations, and the 84.7% Orchestrator Problem
Why 84.7% of citation errors originate at the orchestrator — and how a claim ledger fixes it.
#5805: Freeze the Model, Move the Labels
Your classifier isn't stale — your label set moved. Why retraining is cheap, and why not retraining might be smarter.
#5777: Lorebooks: The Hobbyist RAG That Beat the Labs
How a chat frontend fork built a full retrieval system — keywords, vectors, recursion — so fictional characters would stop forgetting who they are.