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towardsdatascience.com > your-llm-can-return-perfect-json-and-still-be-wrong

Your LLM Can Return Perfect JSON and Still Be Wrong

3+ hour, 6+ min ago   (1426+ words) What I learned after thinking more carefully about Structured Outputs on messy, incomplete data Three weeks after I turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, I noticed that our reconciliation job started…...

Towards Data Science
towardsdatascience.com > loop-engineering-for-rag-the-small-loops-inside-each-step-the-big-loops-across-the-pipeline

Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline

2+ week, 11+ hour ago   (1680+ words) A one-shot pipeline commits to its first try: parse once, retrieve once, generate once, return whatever comes out. When retrieval comes back empty or the answer is half-formed, there is no second chance. A loop gives the pipeline one: notice…...

Towards Data Science
towardsdatascience.com > context-engineering-is-changing-heres-what-it-means-for-data-scientists

Context Engineering Is Changing. Here???s What It Means for Data Scientists

1+ day, 2+ hour ago   (1470+ words) How to apply the latest context engineering guidelines to your day-to-day data science work There are so many positive sides that come with using systems like Claude; all the repetitive, routine coding gets automated, researching is quicker, and debugging becomes…...

Towards Data Science
towardsdatascience.com > 4-claude-skills-every-data-scientist-needs-in-2026

4 Claude Skills Every Data Scientist Needs in 2026

1+ day, 22+ hour ago   (1306+ words) Blueconic sets this cookie as a unique identifier for the BlueConic profile. Four skills worth adding to your workflow today if you don't want to be left behind A couple months ago I wrote about 3 Claude skills every data scientist…...

Towards Data Science
towardsdatascience.com > 10-positions-for-enterprise-rag-that-mainstream-tutorials-get-wrong

10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong

1+ week, 4+ hour ago   (1470+ words) Enterprise Document Intelligence [Vol.1 #M3] – The ten positions the series argues from, and the map of every article that argues them This article is a manifesto of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks....

Towards Data Science
towardsdatascience.com > ai-agents-dont-need-more-context-they-need-typed-context

AI Agents Don???t Need More Context??? They Need Typed Context

1+ week, 9+ hour ago   (1708+ words) A lightweight runtime layer that separates instructions, evidence, memory, and tool output before they reach the model This article is for anyone building agent systems who assemble prompts from multiple sources (retrieved documents, conversation history, tool outputs, or system instructions)…...

Towards Data Science
towardsdatascience.com > why-we-fine-tuned-siglip-and-why-thats-not-always-the-right-call

Why We Fine-Tuned SigLip (And Why That???s Not Always the Right Call)

1+ week, 2+ day ago   (833+ words) LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions. Image classifiers can be built in many ways. The modern default approach is to run images through a third-party API which internally uses a…...

Towards Data Science
towardsdatascience.com > estimating-from-no-data-deriving-a-continuous-score-from-categories-2

Estimating from No Data: Deriving a Continuous Score from Categories

1+ week, 3+ day ago   (938+ words) A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training To be able to illustrate the work, I developed a toy example, which is a non-linear but deterministic…...

Towards Data Science
towardsdatascience.com > scaling-an-integration-pipeline-without-breaking-correctness

How to Scale an Integration Pipeline Without Breaking Correctness

1+ week, 5+ day ago   (1069+ words) A production account of the throughput work behind a 16x jump — and the two guarantees it was never allowed to trade away Two guarantees sat underneath every throughput change, and every one of the optimizations later in this article is built…...

Towards Data Science
towardsdatascience.com > graph-engineering-isnt-about-more-connections-its-about-which-ones-get-used

Graph Engineering Isn???t About More Connections??? It???s About Which Ones Get Used

1+ week, 6+ day ago   (1671+ words) I expected adding more communication pathways between agents to dramatically change network performance. It didn't. Figuring out why turned into a more useful article than the one I set out to write. This article shows a full working implementation in…...