--- title: Medium | daily.dev description: A data scientist's friend was rejected from a job interview not for lacking fundamentals, but for insufficient hands-on experience with Generative AI, LLMs,... canonical: https://daily.dev/posts/medium-74guiqxca twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Medium | daily.dev og:description: A data scientist's friend was rejected from a job interview not for lacking fundamentals, but for insufficient hands-on experience with Generative AI, LLMs,... og:url: https://daily.dev/posts/medium-74guiqxca og:image: https://api.daily.dev/og/posts/74GuiQxCA.png og:image:alt: Medium og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Medium **[Medium](https://daily.dev/sources/medium_js)** · 7 min read · 1 upvotes · 0 comments ## Summary A data scientist's friend was rejected from a job interview not for lacking fundamentals, but for insufficient hands-on experience with Generative AI, LLMs, and agentic frameworks. The post argues that the current hiring market is over-indexing on buzzwords like LangChain and multi-agent orchestration while undervaluing deep ML foundations — even though those foundations are precisely what makes agentic AI systems reliable in production. A concrete RAG pipeline failure example illustrates how ML diagnostic thinking (distinguishing retrieval failures from generation failures from calibration issues) is irreplaceable. The advice for DS/ML practitioners: don't abandon your foundation, but layer agentic AI skills on top of it, including learning the current vocabulary to avoid keyword-based filtering. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does a RAG-based customer support agent give wrong answers in production even though it worked fine in a demo? It usually stems from a retrieval failure rather than a generation failure — the retrieval step returns similar-sounding but wrong documents, the embedding model's similarity threshold is miscalibrated so borderline matches look confident, or the underlying policy documents changed and the index was never refreshed, causing drift. Diagnosing which of these is happening requires classic ML thinking about calibration and data quality, not prompt tweaking. _daily.dev surfaces practical debugging approaches for engineers troubleshooting flaky RAG pipelines in production._ ### Do I still need traditional machine learning skills if I want to work on AI agents and LLM applications? Yes, traditional ML skills remain the foundation that determines whether an agent is reliable or just an impressive demo. Evaluation and metrics, data quality and retrieval, statistical reasoning about hallucination and drift, and model selection or fine-tuning decisions all rely on the same rigor ML practitioners have always needed, just applied to agentic systems instead of standalone models. _daily.dev helps ML practitioners track how agentic AI skills layer onto core data science fundamentals._ ## Similar posts on daily.dev - [5 AI Skills That Will Keep Data Scientists Relevant in 2027](https://daily.dev/posts/5-ai-skills-that-will-keep-data-scientists-relevant-in-2027-aeiffheua) · Towards Data Science · 1 upvotes · 0 comments - [we are building data breach machines and nobody cares](https://daily.dev/posts/we-are-building-data-breach-machines-and-nobody-cares-qcskpagwt) · Hacker News · 3 upvotes · 0 comments - [Microsoft execs warn agentic AI is hollowing out the junior developer pipeline](https://daily.dev/posts/microsoft-execs-warn-agentic-ai-is-hollowing-out-the-junior-developer-pipeline-zfchuraa9) · The New Stack · 1 upvotes · 0 comments - [I Built an AI Agent in a Weekend. It Changed What Recruiters Said to Me.](https://daily.dev/posts/i-built-an-ai-agent-in-a-weekend-it-changed-what-recruiters-said-to-me--pcxznvdbw) · Medium · 2 upvotes · 0 comments - [5 data foundation and technology stack gaps stalling your AI agents](https://daily.dev/posts/5-data-foundation-and-technology-stack-gaps-stalling-your-ai-agents-l9rkmz54j) · elastic · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#agentic-ai](https://daily.dev/tags/agentic-ai) [View this post on daily.dev](https://daily.dev/posts/medium-74guiqxca) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Medium","url":"https://daily.dev/posts/medium-74guiqxca","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/medium-74guiqxca"},"datePublished":"2026-07-22T03:33:53.939Z","dateModified":"2026-09-14T06:26:19.789Z","description":"A data scientist's friend was rejected from a job interview not for lacking fundamentals, but for insufficient hands-on experience with Generative AI, LLMs,...","image":"https://media.daily.dev/image/upload/s--ZrL_HSsR--/f_auto/v1722860399/public/Placeholder%2006","thumbnailUrl":"https://media.daily.dev/image/upload/s--ZrL_HSsR--/f_auto/v1722860399/public/Placeholder%2006","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/medium-74guiqxca","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,data-science,llm,agentic-ai","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Medium"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/medium-74guiqxca#faq","mainEntity":[{"@type":"Question","name":"Why does a RAG-based customer support agent give wrong answers in production even though it worked fine in a demo?","acceptedAnswer":{"@type":"Answer","text":"It usually stems from a retrieval failure rather than a generation failure — the retrieval step returns similar-sounding but wrong documents, the embedding model's similarity threshold is miscalibrated so borderline matches look confident, or the underlying policy documents changed and the index was never refreshed, causing drift. 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