实现人工智能的精准性：利用人工智能代理检索正确的数据.pdf

1、Achieving Precision in AI:Fine tuning your AgenticRAG solutionAdi PolakConfluentAdi PolakAdi PolakConfluentAdi PolakAdi PolakAdi Polak,ConfluentAdi PolakPrecision in traditional MLAdi PolakAdi PolakAdi PolakAuthor of Scaling ML w/Spark(OReilly)People Manager,Software Engineer&Data/MLStreaming,big da

2、ta field,including ML at scaleAdiPolakData-Centric OptimizationArchitectural Enhancements(RAG)Domain-Specific Fine-Tuning Inference OptimizationOtherHow RAG works?How RAG works?Critical componentof RAG:INDEXINGRETRIEVALGENERATIONType of RAGsTermSimilarityGraphAdi PolakVector Search and embedding mod

3、elsAspectChallengeImpactSolutionRetrieval PhaseIrrelevant or outdated resultsPoor context for generationHybrid retrieval(semantic+lexical)Ambiguous queriesMismatched retrievalsQuery expansion or iterative refinementScalability issuesLatency in large datasetsScalable algorithms(e.g.,HNSW,PQ)Augmentat

4、ion PhaseShallow integrationFragmented or redundant contextHierarchical summarizationInformation overloadVerbose or incoherent responsesDocument ranking and filteringToken limitationsTruncated critical informationDynamic context compressionGeneration PhaseHallucinationsIncorrect or misleading answer

5、sRetrieval validation and groundingBias amplificationSkewed outputsCurated,unbiased knowledge basesSystemic ChallengesOutdated dataPropagation of errorsRegular updates to knowledge basesLatency bottlenecksDelayed responsesLow-latency algorithms and cachingLack of transparencyReduced trust in outputs

6、Source attribution and provenance trackingKey Challenges and SolutionsAdi PolakAspectChallengeImpactSolutionRetrieval PhaseIrrelevant or outdated resultsPoor context for generationHybrid retrieval(semantic+lexical)Ambiguous queriesMismatched retrievalsQuery expansion or iterative refinementScalabili