[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-fed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary":3,"summaries-facets-categories":138,"summary-related-fed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary":7042},{"id":4,"title":5,"ai":6,"body":13,"categories":96,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":101,"navigation":117,"path":118,"published_at":119,"question":98,"scraped_at":120,"seo":121,"sitemap":122,"source_id":123,"source_name":124,"source_type":125,"source_url":126,"stem":127,"tags":128,"thumbnail_url":133,"tldr":134,"tweet":135,"unknown_tags":136,"__hash__":137},"summaries\u002Fsummaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary.md","Integrating Gemini Intelligence into AlloyDB via AI Functions",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4880,603,3107,0.0021245,{"type":14,"value":15,"toc":90},"minimark",[16,21,25,28,83,87],[17,18,20],"h2",{"id":19},"bringing-llm-intelligence-to-sql-workflows","Bringing LLM Intelligence to SQL Workflows",[22,23,24],"p",{},"AlloyDB AI functions bridge the gap between static database storage and generative AI by allowing developers to invoke foundation models like Gemini directly within SQL queries. This approach enables complex operations—such as reranking hybrid search results based on external knowledge, filtering transactions for fraud, or converting unstructured text into structured JSON—without needing to extract data to an external application layer.",[22,26,27],{},"Key generally available functions include:",[29,30,31,48,57,66],"ul",{},[32,33,34,38,39,43,44,47],"li",{},[35,36,37],"strong",{},"Ranking & Filtering:"," ",[40,41,42],"code",{},"ai.rank"," (including semantic ranking) and ",[40,45,46],{},"ai.if"," for intelligent, context-aware filtering.",[32,49,50,38,53,56],{},[35,51,52],{},"Generation:",[40,54,55],{},"ai.generate"," for transforming unstructured data into structured formats.",[32,58,59,38,62,65],{},[35,60,61],{},"Forecasting:",[40,63,64],{},"ai.forecast",", powered by the TimesFM model, for predictive analytics on historical data.",[32,67,68,38,71,74,75,78,79,82],{},[35,69,70],{},"Insights:",[40,72,73],{},"ai.analyze_sentiment",", ",[40,76,77],{},"ai.summarize",", and ",[40,80,81],{},"ai.agg_summarize"," for distilling large volumes of text or multi-row data into actionable insights.",[17,84,86],{"id":85},"optimizing-performance-and-cost","Optimizing Performance and Cost",[22,88,89],{},"A primary barrier to using LLMs in databases is the latency and cost of row-by-row API calls. AlloyDB addresses this through \"Optimized AI Functions.\" Instead of calling a remote LLM for every row, the system trains a local model on your specific embeddings and LLM outputs. When a query is executed, the database invokes this local model, which can process up to 100,000 rows per second. Benchmarks indicate this method is up to 23,000 times faster and 6,000 times cheaper than traditional row-at-a-time LLM calls, costing less than one-tenth of a cent per operation. Additional performance acceleration is achieved through asynchronous bulk prompting, AI function acceleration, and array-based processing.",{"title":91,"searchDepth":92,"depth":92,"links":93},"",2,[94,95],{"id":19,"depth":92,"text":20},{"id":85,"depth":92,"text":86},[97],"AI & LLMs",null,"md",false,{"content_references":102,"triage":112},[103,108],{"type":104,"title":105,"url":106,"context":107},"tool","AlloyDB for PostgreSQL","https:\u002F\u002Fcloud.google.com\u002Falloydb","recommended",{"type":109,"title":110,"context":111},"other","TimesFM","mentioned",{"relevance":113,"novelty":114,"quality":114,"actionability":113,"composite":115,"reasoning":116},5,4,4.55,"Category: AI & LLMs. The article provides a deep dive into integrating LLMs with SQL workflows using AlloyDB AI functions, addressing a specific pain point of performance and cost in AI integration. It offers concrete examples of functions that developers can implement immediately, making it highly actionable.",true,"\u002Fsummaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary","2026-06-22 15:35:46","2026-06-23 12:56:35",{"title":5,"description":91},{"loc":118},"fed2d5e03673ece0","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PxbLWePxt40","summaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary",[129,130,131,132],"automation","data-science","ai-llms","postgresql","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FPxbLWePxt40\u002Fhqdefault.jpg","AlloyDB AI functions allow developers to execute LLM-powered tasks like ranking, summarization, and forecasting directly within SQL, using optimized local models to achieve massive performance gains and cost reductions over standard row-by-row LLM calls.","This video provides a technical overview of how to use integrated AI functions within [AlloyDB for PostgreSQL](https:\u002F\u002Fgoo.gle\u002F4d3cuSe) to perform tasks like reranking, sentiment analysis, and forecasting directly via SQL. It explains how the platform optimizes these calls to reduce latency and cost compared to standard row-by-row LLM 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While AI models (like LLMs) provide the interface and reasoning, their performance is strictly bounded by the quality of the data they ingest. This relationship is cyclical: data science prepares the raw information for AI, and AI tools (such as synthetic data generation) are increasingly used to refine and label the data that trains the models.",[17,7061,7063],{"id":7062},"the-document-qa-pipeline-a-practical-reaction","The Document Q&A Pipeline: A Practical Reaction",[22,7065,7066],{},"To build a reliable enterprise Q&A system that avoids hallucinations and respects permissions, one must integrate elements from both data science and AI:",[7068,7069,7070,7113],"ol",{},[32,7071,7072,7075],{},[35,7073,7074],{},"Data Preparation (The Data Science Side):",[29,7076,7077,7083,7089,7095,7101,7107],{},[32,7078,7079,7082],{},[35,7080,7081],{},"ET (Extract, Transform, Load):"," Aggregates scattered documents (SharePoint, Confluence, wikis).",[32,7084,7085,7088],{},[35,7086,7087],{},"DI (Data Ingest):"," Ensures the pipeline remains current with updated policies.",[32,7090,7091,7094],{},[35,7092,7093],{},"CD (Data Cleansing):"," Removes artifacts like OCR junk, watermarks, and headers.",[32,7096,7097,7100],{},[35,7098,7099],{},"ST (Structured Data):"," Chunks documents by section and tags them with metadata (department, date, sensitivity).",[32,7102,7103,7106],{},[35,7104,7105],{},"EN (Data Encoding):"," Converts categorical metadata into filterable formats.",[32,7108,7109,7112],{},[35,7110,7111],{},"GO (Data Governance):"," Enforces strict audit trails and permissioning to prevent unauthorized data exposure.",[32,7114,7115,7118],{},[35,7116,7117],{},"Inference (The AI Side):",[29,7119,7120,7126,7132,7138,7144,7150],{},[32,7121,7122,7125],{},[35,7123,7124],{},"EM (Embeddings):"," Converts cleaned text chunks into vectors for semantic search.",[32,7127,7128,7131],{},[35,7129,7130],{},"VX (Vector Database):"," Stores vectors for runtime retrieval.",[32,7133,7134,7137],{},[35,7135,7136],{},"RG (RAG):"," Retrieves relevant chunks based on user queries.",[32,7139,7140,7143],{},[35,7141,7142],{},"PR (Prompt Template):"," Grounds the model by injecting retrieved chunks into the prompt.",[32,7145,7146,7149],{},[35,7147,7148],{},"LG (LLM):"," Generates the final answer based on the grounded context.",[32,7151,7152,7155],{},[35,7153,7154],{},"GR (Guardrails):"," Acts as a final filter to verify citations and redact PII.",[17,7157,7159],{"id":7158},"closing-the-loop-continuous-improvement","Closing the Loop: Continuous Improvement",[22,7161,7162],{},"Linear pipelines are static. To evolve, systems must incorporate a feedback loop that allows them to learn from failures:",[29,7164,7165,7171,7177],{},[32,7166,7167,7170],{},[35,7168,7169],{},"DR (Data Drift):"," Monitors query embeddings and user feedback to detect when the system's performance deviates from the baseline.",[32,7172,7173,7176],{},[35,7174,7175],{},"Synthetic Data:"," When drift is detected, AI is used to generate synthetic Q&A pairs that specifically address the failing patterns.",[32,7178,7179,7182],{},[35,7180,7181],{},"FT (Fine-Tuning):"," These synthetic pairs are used to retrain the embedding model, ensuring that future queries land closer to the correct document chunks in vector space. This creates a self-improving system that requires minimal manual intervention.",{"title":91,"searchDepth":92,"depth":92,"links":7184},[7185,7186,7187],{"id":7055,"depth":92,"text":7056},{"id":7062,"depth":92,"text":7063},{"id":7158,"depth":92,"text":7159},[97],{"content_references":7190,"triage":7191},[],{"relevance":113,"novelty":114,"quality":114,"actionability":114,"composite":7192,"reasoning":7193},4.35,"Category: AI & LLMs. The article provides a detailed framework for integrating data science and AI in production systems, addressing the audience's need for practical applications in building AI-powered products. It outlines specific steps in the data preparation and inference processes, making it actionable for developers and founders.","\u002Fsummaries\u002F721bc1358521f587-building-production-ai-the-data-science-ai-loop-summary","2026-08-13 11:00:17","2026-08-14 03:20:42",{"title":7045,"description":91},{"loc":7194},"721bc1358521f587","IBM Technology","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jAkB_qeATag","summaries\u002F721bc1358521f587-building-production-ai-the-data-science-ai-loop-summary",[130,129,131,7204],"rag","Production-ready AI systems rely on a continuous feedback loop where robust data science pipelines (ETL, governance) feed AI models, and AI, in turn, generates synthetic data to improve those same pipelines.","The presenters use a \"periodic table\" metaphor to map out the standard components of data engineering and AI pipelines. They walk through a document Q&A use case to demonstrate how traditional data science tasks (ETL, cleansing, governance) and modern AI techniques (embeddings, RAG, guardrails) function as a unified, iterative system.",[131,7204],"tY8zEjrhgWlhc8cI6LtGblaB3FT6FjpJ7G7dp7UPb-Q",{"id":7210,"title":7211,"ai":7212,"body":7217,"categories":7268,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7269,"navigation":117,"path":7275,"published_at":7276,"question":98,"scraped_at":7276,"seo":7277,"sitemap":7278,"source_id":7279,"source_name":7280,"source_type":7281,"source_url":7282,"stem":7283,"tags":7284,"thumbnail_url":98,"tldr":7286,"tweet":98,"unknown_tags":7287,"__hash__":7288},"summaries\u002Fsummaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary.md","Evaluating LLM Agents in High-Stakes Energy Analytics",{"provider":7,"model":8,"input_tokens":7213,"output_tokens":7214,"processing_time_ms":7215,"cost_usd":7216},6041,462,3132,0.00220325,{"type":14,"value":7218,"toc":7263},[7219,7223,7226,7230,7233,7253,7256,7260],[17,7220,7222],{"id":7221},"bridging-the-gap-in-domain-specific-agent-benchmarking","Bridging the Gap in Domain-Specific Agent Benchmarking",[22,7224,7225],{},"Most existing agent benchmarks focus on general-purpose tasks or static knowledge recall. This research addresses a critical void in the energy sector, which demands high-stakes, real-world capabilities: live data retrieval, complex regulatory interpretation, and multi-step quantitative reasoning. The authors introduce a new evaluation environment comprising 243 expert-curated problems designed to test how LLM agents perform when equipped with specialized domain tools.",[17,7227,7229],{"id":7228},"the-evaluation-framework","The Evaluation Framework",[22,7231,7232],{},"The benchmark categorizes tasks into three distinct domains to stress-test agentic reasoning:",[29,7234,7235,7241,7247],{},[32,7236,7237,7240],{},[35,7238,7239],{},"Market Data Retrieval and Analysis:"," Involves interacting with live electricity market APIs from major U.S. Independent System Operators (ISOs).",[32,7242,7243,7246],{},[35,7244,7245],{},"Knowledge Retrieval and Interpretation:"," Focuses on navigating regulatory dockets and utility tariff databases using retrieval-augmented generation (RAG).",[32,7248,7249,7252],{},[35,7250,7251],{},"Advanced Quantitative Modeling:"," Requires agents to perform asset revenue estimation, hedging strategy analysis, and optimization modeling.",[22,7254,7255],{},"To ensure rigorous assessment, the authors employ a multi-dimensional protocol that scores agents based on approach correctness, answer accuracy, attribute alignment, and source validity. The framework utilizes category-aware routing, ensuring that the scoring criteria are tailored to the specific nature of the task (e.g., quantitative vs. qualitative).",[17,7257,7259],{"id":7258},"insights-on-tool-augmented-performance","Insights on Tool-Augmented Performance",[22,7261,7262],{},"The study provides a comparative analysis of both closed-source and open-source models, specifically examining the interaction between model reasoning capabilities and domain-specific tooling. By releasing the benchmark and its associated artifacts, the authors aim to establish a reproducible standard for evaluating AI agents in professional, high-stakes environments where accuracy and source reliability are paramount.",{"title":91,"searchDepth":92,"depth":92,"links":7264},[7265,7266,7267],{"id":7221,"depth":92,"text":7222},{"id":7228,"depth":92,"text":7229},{"id":7258,"depth":92,"text":7259},[97],{"content_references":7270,"triage":7271},[],{"relevance":113,"novelty":114,"quality":114,"actionability":7272,"composite":7273,"reasoning":7274},3,4.15,"Category: AI & LLMs. The article presents a new benchmark for evaluating LLM agents in the energy sector, addressing a specific audience pain point regarding the application of AI in high-stakes environments. It offers insights into the evaluation framework and performance metrics, which can inform product builders about the capabilities of AI agents in specialized domains.","\u002Fsummaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary","2026-06-26 12:58:19",{"title":7211,"description":91},{"loc":7275},"adc718e7c1350c24","arXiv cs.AI","article","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.26346","summaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary",[7285,130,129,131],"agents","A new benchmark of 243 expert-curated energy tasks reveals how tool-augmented LLM agents handle live data, regulatory knowledge, and quantitative modeling in professional energy markets.",[131],"VDI34L-g_eZ2d8zUgITK4_q10cI79IQ98SWVq_0tGqc",{"id":7290,"title":7291,"ai":7292,"body":7297,"categories":7371,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7372,"navigation":117,"path":7383,"published_at":7384,"question":98,"scraped_at":7384,"seo":7385,"sitemap":7386,"source_id":7387,"source_name":7388,"source_type":7281,"source_url":7389,"stem":7390,"tags":7391,"thumbnail_url":98,"tldr":7393,"tweet":98,"unknown_tags":7394,"__hash__":7395},"summaries\u002Fsummaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary.md","Hands-On Guide to FineWeb Corpus Processing and Analytics",{"provider":7,"model":8,"input_tokens":7293,"output_tokens":7294,"processing_time_ms":7295,"cost_usd":7296},11152,651,3432,0.0037645,{"type":14,"value":7298,"toc":7367},[7299,7303,7318,7321,7341,7345,7352],[17,7300,7302],{"id":7301},"efficient-corpus-handling-and-quality-filtering","Efficient Corpus Handling and Quality Filtering",[22,7304,7305,7306,7309,7310,7313,7314,7317],{},"To manage multi-terabyte datasets without local storage constraints, use the Hugging Face ",[40,7307,7308],{},"datasets"," library in ",[40,7311,7312],{},"streaming=True"," mode. This allows for iterative processing of samples, such as the ",[40,7315,7316],{},"sample-10BT"," subset of FineWeb.",[22,7319,7320],{},"Quality filtering is essential for LLM training. You can reproduce production-grade pipelines by applying three categories of heuristics:",[29,7322,7323,7329,7335],{},[32,7324,7325,7328],{},[35,7326,7327],{},"Gopher-style:"," Filters based on word count (50–100k range), mean word length (3–10 characters), symbol density, bullet point frequency, and stopword presence.",[32,7330,7331,7334],{},[35,7332,7333],{},"C4-style:"," Removes boilerplate text (e.g., \"lorem ipsum\", \"javascript is disabled\") and excessive brace usage.",[32,7336,7337,7340],{},[35,7338,7339],{},"Custom Logic:"," Evaluates line-level quality by calculating duplicate line fractions (rejecting if > 30%) and identifying list-heavy content (rejecting if > 67% of lines are short).",[17,7342,7344],{"id":7343},"deduplication-and-tokenization-validation","Deduplication and Tokenization Validation",[22,7346,7347,7348,7351],{},"Near-duplicate detection is critical to prevent data leakage and model memorization. Using ",[40,7349,7350],{},"datasketch",", you can implement a MinHash-based LSH (Locality Sensitive Hashing) pipeline. By converting text into word shingles (k=5) and generating MinHash signatures, you can identify document pairs with a Jaccard similarity above a set threshold (e.g., 0.7).",[22,7353,7354,7355,7358,7359,7362,7363,7366],{},"Validating metadata is equally important. By recomputing token counts using ",[40,7356,7357],{},"tiktoken"," (specifically the ",[40,7360,7361],{},"gpt2"," encoding), you can verify the integrity of stored ",[40,7364,7365],{},"token_count"," fields. Small discrepancies are expected due to tokenizer versioning, but consistent results confirm the dataset's reliability. Calculating the \"characters per token\" ratio provides a useful metric for assessing tokenizer compression efficiency across different domains.",{"title":91,"searchDepth":92,"depth":92,"links":7368},[7369,7370],{"id":7301,"depth":92,"text":7302},{"id":7343,"depth":92,"text":7344},[97],{"content_references":7373,"triage":7381},[7374,7377,7379],{"type":104,"title":7375,"url":7376,"context":107},"FineWeb Dataset","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FHuggingFaceFW\u002Ffineweb",{"type":104,"title":7350,"url":7378,"context":107},"https:\u002F\u002Fekzhu.github.io\u002Fdatasketch\u002F",{"type":104,"title":7357,"url":7380,"context":107},"https:\u002F\u002Fgithub.com\u002Fopenai\u002Ftiktoken",{"relevance":113,"novelty":114,"quality":114,"actionability":113,"composite":115,"reasoning":7382},"Category: AI & LLMs. The article provides a hands-on guide for processing and analyzing large-scale web datasets, which is directly relevant to building AI-powered products. It includes specific techniques for quality filtering and deduplication that can be immediately applied in LLM training workflows.","\u002Fsummaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary","2026-06-15 12:56:59",{"title":7291,"description":91},{"loc":7383},"7b59cd8e4387736f","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F14\u002Fa-coding-hands-on-on-fineweb-for-streaming-filtering-deduplication-tokenization-and-large-scale-web-corpus-analytics\u002F","summaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary",[7392,130,129,131],"python","Learn to stream, filter, deduplicate, and analyze large-scale web datasets like FineWeb using Python, MinHash, and tiktoken to prepare high-quality data for LLM training.",[131],"daAnSA8_x9HlX8ghCmH4Z83DgjXLdbAemd6jWliqFn4",{"id":7397,"title":7398,"ai":7399,"body":7405,"categories":7433,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7434,"navigation":117,"path":7439,"published_at":7440,"question":98,"scraped_at":7441,"seo":7442,"sitemap":7443,"source_id":7444,"source_name":7445,"source_type":7281,"source_url":7446,"stem":7447,"tags":7448,"thumbnail_url":98,"tldr":7449,"tweet":98,"unknown_tags":7450,"__hash__":7451},"summaries\u002Fsummaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary.md","AI Amplifies Bad Data—Fix It First",{"provider":7,"model":7400,"input_tokens":7401,"output_tokens":7402,"processing_time_ms":7403,"cost_usd":7404},"x-ai\u002Fgrok-4.1-fast",5216,1258,13939,0.0016497,{"type":14,"value":7406,"toc":7428},[7407,7411,7414,7418,7421,7425],[17,7408,7410],{"id":7409},"data-quality-drives-85-of-ai-failures","Data Quality Drives 85% of AI Failures",[22,7412,7413],{},"Organizations rushing into AI overlook that 77% report data quality as \"average at best\" (up from 66% last year), only 15% of large enterprise executives believe their data suffices for goals, 26% of enterprise data is \"dirty,\" 94% suspect inaccurate customer data, and 85% of AI projects fail due to poor data. No company lacks data quality issues. AI operationalizes these flaws: messy lending data leads to approving bad loans, duplicative sales data misprioritizes customers, and broken metrics optimize flawed processes. Trusting confident but wrong AI outputs industrializes bad decisions that stayed contained in traditional reports and dashboards.",[17,7415,7417],{"id":7416},"ais-semantic-processing-exposes-data-costs","AI's Semantic Processing Exposes Data Costs",[22,7419,7420],{},"Unlike cheap, deterministic SQL queries scanning 10,000 rows in milliseconds with near-zero marginal cost, AI uses GPU-heavy semantic search: it embeds data into vectors, performs matrix multiplications for inference, and synthesizes proactive insights like spotting outliers, seasonal spikes, or correlations without explicit queries. This makes AI 10x more energy-intensive per query, billing for cognition—tokens processed, context maintained, reasoning performed—scaling like tireless labor. Dirty data forces repeated heavy processing in evolving conversations, shifting economics from FinOps-style cost reduction (store, query, pay per run) to usage → output → value, where data quality determines real returns.",[17,7422,7424],{"id":7423},"reframe-ai-management-around-data-not-symptoms","Reframe AI Management Around Data, Not Symptoms",[22,7426,7427],{},"Fears of AI costs, skills gaps, and security mask root data problems; rising costs signal inefficient processing of messy data, variable outputs reveal inaccuracies, and slowed adoption ignores symptoms. Traditional IT models (cost → efficiency → reduction) fail for probabilistic, consumption-based AI fueled by imperfect data. Leaders must prioritize data cleaning to avoid AI confidently recommending actions like shutting profitable lines based on flawed inputs. AI acts as an unavoidable mirror: fix data to capture its value, or scale mistakes.",{"title":91,"searchDepth":92,"depth":92,"links":7429},[7430,7431,7432],{"id":7409,"depth":92,"text":7410},{"id":7416,"depth":92,"text":7417},{"id":7423,"depth":92,"text":7424},[],{"content_references":7435,"triage":7436},[],{"relevance":114,"novelty":7272,"quality":114,"actionability":7272,"composite":7437,"reasoning":7438},3.6,"Category: Data Science & Visualization. The article discusses the critical importance of data quality in AI implementations, addressing a specific pain point for product builders who need to ensure their data is clean before deploying AI solutions. It provides insights into the consequences of poor data but lacks detailed actionable steps for improving data quality.","\u002Fsummaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary","2026-04-20 16:23:11","2026-04-21 15:26:26",{"title":7398,"description":91},{"loc":7439},"6c1cdcac335f19f8","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fdont-be-afraid-of-ai-be-terrified-of-your-data-97569858b42f?source=rss----32881626c9c9---4","summaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary",[130,131],"AI doesn't fix poor data quality; it scales the errors, leading to wrong decisions like approving bad loans or prioritizing wrong customers. 85% of AI failures stem from bad data, so clean data before adopting AI.",[131],"pXNZvNlaf4m9AWQ5hD6eh2a7NtGiLwpEY1JU9QQSt6c"]