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<div style="display: none; max-height: 0px; overflow: hidden;">Query-adaptive RAG enables enterprise retrieval-augmented generation systems to dynamically classify and route queries based on complexity </div>
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<h1><strong>TLDR Data <span id="date">2026-01-22</span></strong></h1>
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<strong>👩🏭 Build fast, scale right: Meet your AI app & agent factory (Sponsor)</strong>
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Agent adoption is accelerating. Winning teams ship multi‑agent systems with observability and governance built in from the start. <p></p><p><a class="Hyperlink SCXW179431237 BCX0" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fai.azure.com%2F%3Focid=cmmxp9rfcmy/3/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/AldHKpvH3NV4Dsf5Bec0ZQOMWImZvkU1QsuhQH8f7ng=441" rel="noreferrer noopener" target="_blank"><span>Microsoft Foundry</span></a> is a modular, interoperable platform to build, optimize, and govern all your agents from day one. With Foundry, you can securely ground your AI apps and agents on data stored in any location while respecting user permissions and data classification policies. Foundry Control Plane ensures you can evaluate, monitor, and optimize AI apps and agents in real-time. </p>
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<h1><strong>Deep Dives</strong></h1>
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<strong>Query-Adaptive RAG: Routing Complex Questions to Multi-Hop Retrieval While Keeping Simple Queries Fast (8 minute read)</strong>
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Query-adaptive RAG enables enterprise retrieval-augmented generation systems to dynamically classify and route queries based on complexity, shifting between fast single-hop retrieval for factual questions and multi-hop synthesis for reasoning-heavy tasks. Leading implementations report 30-40% latency reductions and 8% accuracy gains, with precision on factual queries reaching 92-96% and LLM call costs dropping by up to 50%. Success hinges on robust complexity detection, tailored routing orchestration, and continuous feedback loops.
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<strong>Your Agent's Reasoning Is Fine - Its Memory Isn't (17 minute read)</strong>
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Production incident response is slowed not by lack of fixes but by scattered context and undocumented dependencies. Integrating an AI agent powered by GraphRAG enables rapid, structured retrieval of service relationships, ownership, past incidents, and documentation. This system, triggered by monitoring tools like Prometheus and interfacing via FastAPI and Slack, transforms incident alerts into actionable, context-rich reports within seconds.
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<strong>Apache Spark Performance Tuning on Amazon EMR (21 minute read)</strong>
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Optimal Spark performance on EMR demands strategic tuning, rather than additional compute, to prevent wasted costs and degradation from shuffle overhead, GC pressure, and network contention. Key optimizations include executor sizing, memory management, partition and shuffle tuning, caching, column/prune elimination, predicate pushdown, handling small files, and leveraging Z-ordering for “haystack queries”. Benchmarks show a 10x difference in 1TB S3 read times based solely on data organization and configuration.
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A tutorial on how to connect an AI tool to MotherDuck via an MCP server so you can ask questions in plain English and have the AI run read-only SQL to explore data, fix query errors automatically, and return insights fast. This is demonstrated on a huge table, private S3 Parquet files (including vector similarity search), and even public APIs that can be queried and parsed like tables.
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<strong>You Probably Don't Need a Vector Database for Your RAG — Yet (6 minute read)</strong>
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For small-to-medium RAG pipelines, NumPy and scikit-learn can deliver millisecond-level in-memory vector search on millions of records, eliminating the overhead and complexity of dedicated vector databases like Pinecone or Weaviate. Using pure matrix multiplication and tree-based search (KD/Ball-Tree), typical workloads up to ~1.5GB of embeddings (e.g., 1M vectors × 384-dim) perform efficiently without network serialization or CRUD demands. Transition to vector databases only when persistence, high-frequency updates, metadata filtering, or RAM limits are required.
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<strong>Agents that don't suck (Sponsor)</strong>
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<strong>mviz (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
mviz is a lightweight tool that lets you turn small JSON specs into polished HTML or PDF reports through Claude, so you can explore data, iterate quickly, and share results without building dashboards or infrastructure. It focuses on fast, AI-native analysis workflows with minimal boilerplate and high-quality static outputs that are easy to export and reuse.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhakibenita.com%2Fpostgresql-unconventional-optimizations%3Futm_source=tldrdata/1/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/gwQTncQmiLjfF-hRNQ_H1ft9IxPcDGsbZj1jaUO6YKM=441">
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<strong>Unconventional PostgreSQL Optimizations (11 minute read)</strong>
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<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Unconventional PostgreSQL techniques can deliver big performance gains, such as skipping impossible scans using constraints, indexing only the expressions you actually query to shrink indexes, and enforcing uniqueness on large values with hash-backed constraints. Thoughtful schema and index design often beats the default habit of adding large B-tree indexes everywhere.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.infoq.com%2Fnews%2F2026%2F01%2Fprisma-7-performance%2F%3Futm_source=tldrdata/1/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/FgR2CVgQK_hQT_idAQ39bN4Sw65vuIYqV1z7VX9cTLU=441">
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<strong>Prisma 7: Rust-Free Architecture and Performance Gains (3 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Prisma, the TypeScript-first ORM, shifts away from Rust in its release 7.0, delivering a 90% reduction in bundle size and 3x faster query execution, while cutting CPU and memory loads. Generated code now resides outside node_modules, enabling faster, restart-free development workflows, and type generation is 70% quicker, evaluating 98% fewer types.
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<div style="text-align: center;"><span style="font-size: 36px;">🎁</span></div></div>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.rilldata.com%2Fblog%2Fhow-clickhouse-became-one-of-the-fastest-growing-databases-in-the-world%3Futm_source=tldrdata/1/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/R51rrMwB7g-2c-UZsve-DpbTwK7xab1x-EfWSXIofP8=441">
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<strong>How ClickHouse became one of the fastest-growing databases in the world (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
ClickHouse grew by solving a problem most databases could not: running complex analytics at massive scale with real-time, millisecond latency. Its strong fit for AI workloads, simple developer experience, and obsessive engineering focus from its creator have made it a default choice for fast-growing companies that need performance without operational complexity.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fposts%2Foleg-agapov_i-almost-stopped-using-excelgoogle-sheets-activity-7419774557261262848-tDnY%3Futm_source=tldrdata/1/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/6UT04-i8zZzPW1e7qTR9t_nbwkhEieCUeEHNVZOFCkU=441">
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<strong>I almost stopped using Excel/Google Sheets for working with CSV (1 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Switching from Excel and Google Sheets to DuckDB and SQL for working with CSVs is possible. Simple queries like select distinct can replace manual spreadsheet steps and make exporting results trivial. SQL over files is faster, cleaner, and less painful than spreadsheet workflows for common data tasks.
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<div style="text-align: center;"><span style="font-size: 36px;">⚡</span></div></div>
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<h1><strong>Quick Links</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.montecarlodata.com%2Fblog-rag-vs-fine-tuning%2F%3Futm_source=tldrdata/1/0100019be5633c94-818f4809-f676-4a15-8e42-575fa2e5e477-000000/TOrgYxTFzi_ug4ugyBG92hhLgczUfRAsjlGvzLcXmHc=441">
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<strong>RAG vs Fine Tuning: How to Choose the Right Method (28 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Side-by-side comparison clarifies when dynamic retrieval or model weight adaptation delivers the best ROI.
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<strong>Docker lazy loading at Grab: Accelerating container startup times (9 minute read)</strong>
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Details a SOCI-powered rollout that cut image pulls by 4x and boosted autoscaling responsiveness.
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