WEBVTT 00:00.120 --> 00:01.080 Retrieval. 00:01.080 --> 00:09.120 Augmented generation has fundamentally changed how we build AI systems, especially when accuracy and 00:09.120 --> 00:10.720 reliability matter. 00:11.360 --> 00:18.040 While basic Rag approaches work well for demos and proofs of concept, they often break down in real 00:18.040 --> 00:19.520 production environments. 00:20.280 --> 00:26.400 This presentation focuses on the techniques that move rag from experimental to enterprise grade. 00:26.920 --> 00:33.480 We'll explore why naive vector search alone is not enough, and how advanced methods such as hybrid 00:33.480 --> 00:39.840 search, reranking, and multi-document reasoning dramatically improve system performance. 00:40.480 --> 00:46.600 These techniques allow AI systems not just to retrieve information, but to reason over it in a way 00:46.600 --> 00:49.800 that is precise, scalable, and trustworthy. 00:50.360 --> 00:56.840 The goal is to help you understand what separates a chatbot that sometimes works from an intelligent 00:56.880 --> 01:02.570 knowledge system that can be confidently Deployed across real business workflows. 01:03.330 --> 01:07.090 Basic Rag performs well under ideal conditions. 01:07.490 --> 01:13.890 Simple factual queries, single document lookups, and direct question answer pairs are usually handled 01:13.890 --> 01:14.610 correctly. 01:15.050 --> 01:18.530 However, real world queries are rarely that clean. 01:18.890 --> 01:26.010 Users ask complex multi-part questions, work with long documents, and often provide ambiguous or incomplete 01:26.010 --> 01:26.690 intent. 01:27.170 --> 01:33.490 In these situations, Rag systems fail not because the language model is weak, but because the retrieval 01:33.490 --> 01:34.490 step is flawed. 01:35.010 --> 01:41.330 Relevant context may be missing due to poor chunking or embedding limitations, or correct documents 01:41.330 --> 01:44.450 may be retrieved but ranked too low to be used. 01:44.930 --> 01:50.570 The most important takeaway from this slide is that most rag failures are retrieval failures. 01:51.130 --> 01:57.850 Improving what information reaches the model is often far more impactful than improving prompts or switching 01:57.850 --> 01:58.490 models. 01:58.870 --> 02:02.550 Vector search excels at capturing semantic meaning. 02:02.990 --> 02:09.070 It understands that similar concepts and phrases are related, even if they don't share the same words. 02:09.590 --> 02:13.790 However, this strength becomes a weakness when precision is required. 02:14.270 --> 02:21.310 Vector search struggles with exact keyword matching identifiers like error codes or policy numbers. 02:21.550 --> 02:27.590 Rare domain specific terms and numeric precision such as dates or measurements. 02:28.190 --> 02:34.670 For example, a query asking for a specific error code in a specific policy version may return loosely 02:34.670 --> 02:38.510 related discussions, but miss the exact reference entirely. 02:38.990 --> 02:41.150 This highlights a critical limitation. 02:41.390 --> 02:46.710 Semantic similarity alone cannot meet the precision demands of enterprise applications. 02:47.150 --> 02:49.670 Production systems require more than meaning. 02:49.870 --> 02:51.710 They require exactness. 02:51.910 --> 02:57.590 Hybrid search solves the precision problem by combining two complementary approaches. 02:58.080 --> 03:05.160 Keyword search, such as bm25, provides exact term matching and strong precision for identifiers and 03:05.160 --> 03:06.160 structured data. 03:06.680 --> 03:11.320 Vector search contributes semantic understanding and contextual similarity. 03:11.880 --> 03:18.200 Hybrid search executes both in parallel, then intelligently fuses the results using techniques like 03:18.200 --> 03:20.960 reciprocal rank fusion or weighted scoring. 03:21.560 --> 03:25.440 The final ranked list balances both precision and recall. 03:25.880 --> 03:31.280 This approach ensures that exact matches are not missed while still capturing semantically relevant 03:31.280 --> 03:32.040 content. 03:32.520 --> 03:39.120 Many production tools now support hybrid search natively, including Elasticsearch, We, V8, and Azure 03:39.160 --> 03:40.120 AI search. 03:40.400 --> 03:45.160 Hybrid search is the foundation of reliable enterprise Rag systems. 03:45.600 --> 03:52.960 Hybrid search should be deployed whenever user queries include exact keywords, numbers, or identifiers. 03:53.480 --> 03:55.960 This is common in domains such as legal. 03:56.060 --> 04:00.940 Compliance, technical documentation and enterprise knowledge bases. 04:01.580 --> 04:08.540 Product SKUs, version numbers, policy clauses, and structured terminology all demand precision. 04:09.420 --> 04:13.060 The decision rule is simple if exact terms matter. 04:13.180 --> 04:15.060 Hybrid search is not optional. 04:15.380 --> 04:19.260 It is essential from a performance standpoint. 04:19.260 --> 04:25.980 Hybrid search adds minimal latency when properly indexed, typically under 20 milliseconds, while it 04:25.980 --> 04:28.700 requires additional storage for dual indexes. 04:28.940 --> 04:32.700 The improvement in result quality far outweighs the cost. 04:33.580 --> 04:39.620 Tuning the balance between keyword and vector scoring allows teams to optimize performance based on 04:39.620 --> 04:41.420 real evaluation metrics. 04:41.700 --> 04:47.100 Initial retrieval is designed for speed and recall, not perfect accuracy. 04:47.740 --> 04:53.220 It intentionally casts a wide net, returning many potentially relevant results. 04:53.660 --> 05:00.270 Reranking is the second stage that transforms this approximate retrieval into precise selection. 05:00.910 --> 05:07.870 In this phase, more computationally expensive models evaluate true relevance and select only the best 05:07.870 --> 05:09.750 chunks to pass to the LLM. 05:10.430 --> 05:17.670 Reranking can be implemented using cross encoder models, LLM based scoring, or rule based heuristics. 05:18.270 --> 05:25.020 Although Reranking adds some latency and compute cost, it typically improves answer accuracy by 15 05:25.020 --> 05:29.510 to 30% in production systems for enterprise use cases. 05:29.710 --> 05:32.150 This trade off is almost always worth it. 05:32.270 --> 05:38.070 Many enterprise questions require synthesizing information across multiple documents. 05:38.430 --> 05:45.350 Examples include comparing policies, tracking changes over time, or summarizing insights across reports. 05:45.910 --> 05:52.750 Basic Rag architectures retrieve chunks independently, making this kind of reasoning extremely difficult 05:53.150 --> 05:59.400 without document level, Context or cross-reference signals, the model cannot reliably compare or aggregate 05:59.400 --> 06:00.280 information. 06:00.720 --> 06:07.200 Multi-document reasoning requires aggregation, synthesis, and explicit tracking of relationships across 06:07.200 --> 06:07.800 sources. 06:08.240 --> 06:13.520 It also requires attribution, so claims can be traced back to their original documents. 06:14.120 --> 06:20.480 These requirements expose the limitations of simple retrieval pipelines and motivate more advanced Rag 06:20.480 --> 06:21.560 architectures. 06:21.560 --> 06:26.560 Implementing multi-document reasoning requires deliberate architectural choices. 06:26.960 --> 06:33.440 Systems must retrieve content from multiple sources intentionally, often using metadata or document 06:33.440 --> 06:34.400 aware ranking. 06:34.760 --> 06:41.040 Structured prompting helps by forcing the model to extract information from each source before comparing 06:41.040 --> 06:42.640 or synthesizing results. 06:43.280 --> 06:49.360 Complex queries are best handled by breaking them into smaller subtasks with intermediate outputs. 06:49.880 --> 06:52.360 Advanced patterns such as MapReduce, Rag. 06:52.600 --> 06:58.900 Iterative retrieval, and document grouping allow systems to reason across many documents while preserving 06:58.900 --> 07:00.380 context and balance. 07:00.900 --> 07:08.180 A critical best practice is forcing explicit citations, which improves accuracy and enables verification. 07:08.660 --> 07:13.540 Production grade Rag requires moving beyond basic vector search. 07:13.900 --> 07:20.700 Hybrid search improves both precision and recall, especially for structured and identifier heavy domains. 07:21.100 --> 07:25.980 Reranking transforms noisy retrieval into accurate context selection. 07:26.500 --> 07:32.900 Multi-document reasoning unlocks complex enterprise workflows that basic Rag cannot support. 07:33.340 --> 07:40.060 Together, these techniques represent a paradigm shift from simple retrieval to genuine reasoning over 07:40.100 --> 07:40.780 knowledge. 07:41.300 --> 07:45.660 This is the difference between a chatbot and an intelligent knowledge worker. 07:46.340 --> 07:52.700 Building reliable Rag systems means engineering the retrieval pipeline with the same care as the model 07:52.700 --> 07:53.300 itself.