Title: AI Search Results Anki — LearnCoachAssist

Q: What percentage of the combined search market does AI search account for as of June 2025?
A: AI search traffic accounts for 7.82% of the combined search market as of June 2025.

Q: What is Neural Information Retrieval?
A: Neural Information Retrieval is an AI approach where search engines use neural networks to understand and retrieve information more efficiently, forming a cornerstone of modern AI search systems.

Q: What are the five new retrieval tasks introduced for Artificial General Intelligence?
A: The five new retrieval tasks include External Information Retrieval, which allows AI agents to access new information that was not seen during training.

Q: What is the forecast period for when AI search might surpass Google?
A: The forecast extends from 2025 to 2030, projecting when ChatGPT and AI-powered search will surpass traditional search engines like Google.

Q: What is Google's Speech-to-Retrieval engine?
A: A 2025 Google innovation that directly interprets voice search and retrieves information without intermediate text conversion, enabling more natural voice-based search interactions.

Q: What role does Information Retrieval play in AI development?
A: Information Retrieval (IR) is a cornerstone of AI, enabling systems to efficiently find, access, and process relevant data from vast datasets.

Q: What are the key characteristics of advanced AI search solutions in 2025?
A: They deliver faster, smarter, and more accurate results using machine learning, natural language processing, and context understanding.

Q: How does generative AI impact traditional search outcomes?
A: Generative AI transforms search by understanding specific inputs including Boolean terms, connectors, and filters, providing more contextually relevant results.

Q: What is happening in China's AI search market according to Omdia 2025?
A: Chinese AI search products are integrating Generative AI with conventional internet search, creating hybrid search solutions tailored to the Chinese market.

Q: What is External Information Retrieval in AGI context?
A: A capability that allows AI agents to access new information unseen during training, enabling continuous learning and up-to-date knowledge retrieval.

Q: How does AI search market growth compare to traditional search?
A: AI search has grown to capture 7.82% of the search market by mid-2025, showing significant but still emerging adoption compared to established engines.

Q: What technologies enable modern AI search engines?
A: Neural networks, machine learning, natural language processing (NLP), vector embeddings, and transformer models power modern AI search capabilities.

Q: What advantages does AI search have over keyword-based search?
A: AI search understands context, intent, and semantic meaning rather than just matching keywords, providing more relevant and personalized results.

Q: What is the difference between traditional search and AI-powered search?
A: Traditional search relies on keyword matching and PageRank, while AI search uses semantic understanding and machine learning to interpret queries and results.

Q: How do AI search engines handle voice queries?
A: Through technologies like Google's Speech-to-Retrieval engine that directly converts spoken language to search intent without intermediate text processing.

Q: What are the main challenges facing AI search adoption?
A: Challenges include accuracy concerns, computational costs, integration with existing systems, and user trust in AI-generated results.

Q: What role do embeddings play in AI search?
A: Embeddings convert text into numerical vector representations that capture semantic meaning, enabling AI to find conceptually similar content beyond keyword matches.

Q: How does AI search improve personalization?
A: By learning from user behavior, context, and preferences to deliver results tailored to individual needs and search patterns.

Q: What is retrieval-augmented generation (RAG) in AI search?
A: RAG combines retrieval of relevant documents with generative AI to produce accurate, context-aware answers grounded in retrieved information.

Q: What major tech companies are competing in AI search?
A: Google, OpenAI/Microsoft, Perplexity, and Chinese tech giants are leading the development of AI-powered search technologies.

Q: How do AI search engines handle citations and sources?
A: Advanced AI search systems provide direct citations and links to original sources, allowing users to verify information and explore further.

Q: What impact does AI search have on SEO and content discovery?
A: AI search shifts focus from keyword optimization to semantic relevance and user intent, changing how content is discovered and ranked.

Q: What is the role of context in AI search results?
A: AI search uses conversation history, user preferences, and search context to provide more relevant and personalized results over time.

Q: How do vector databases support AI search?
A: Vector databases store and efficiently retrieve embeddings, enabling fast semantic search across millions of documents using similarity matching.

Q: What are multimodal search capabilities in AI?
A: Multimodal AI search can understand and retrieve information across text, images, audio, and video formats, not just text-based queries.

Q: How does AI search handle real-time information?
A: Through External Information Retrieval capabilities that access current data from the web, APIs, and live sources beyond training data.

Q: What is the difference between semantic search and lexical search?
A: Lexical search matches exact words and phrases, while semantic search understands meaning and concepts, finding relevant results even without exact keyword matches.

Q: How do AI search engines combat misinformation?
A: By using source verification, citation linking, confidence scoring, and retrieval from authoritative sources to ensure accuracy.

Q: What is cross-lingual information retrieval?
A: AI systems that can search and retrieve information across multiple languages, understanding queries in one language and finding relevant content in others.

Q: How does AI search impact user privacy concerns?
A: AI search raises concerns about data collection for personalization, query storage, and profiling, leading to debates about privacy vs. personalization benefits.

Q: What are zero-shot and few-shot learning in search AI?
A: Techniques that allow search systems to handle new types of queries without extensive retraining, adapting quickly to new domains and languages.

Q: How does AI search handle ambiguous queries?
A: By using context clues, clarification questions, and multiple intent detection to provide relevant results or ask for clarification when needed.

Q: What is the future outlook for AI search market share?
A: Projections suggest AI search will grow significantly through 2030, potentially competing equally with or surpassing traditional search engines.

Q: How do businesses prepare for AI search dominance?
A: By optimizing content for semantic relevance, structured data, and question-answering rather than traditional keyword SEO strategies.

Q: How does dense retrieval differ from sparse retrieval in modern AI search?
A: Sparse retrieval (e.g. BM25) matches exact lexical tokens, while dense retrieval encodes queries and documents into vector embeddings and matches by cosine similarity, capturing semantic meaning.

Q: What is BM25 and why is it still relevant in 2025 AI search pipelines?
A: BM25 is a probabilistic term-frequency ranking function; it remains a strong baseline and is often hybridized with neural retrievers because it excels at exact-match recall on rare terms.

Q: Define hybrid search and explain why it is the dominant production pattern.
A: Hybrid search combines BM25 + dense vector retrieval (and sometimes cross-encoder reranking) to get lexical precision and semantic recall, with reciprocal rank fusion (RRF) merging the two ranked lists.

Q: What is reciprocal rank fusion (RRF)?
A: RRF merges multiple ranked lists by summing 1 / (k + rank_i) across retrievers, where k is typically 60; it requires no score calibration and is robust across heterogeneous rankers.

Q: Explain the bi-encoder vs. cross-encoder trade-off in retrieval.
A: A bi-encoder encodes query and documents independently for fast ANN search but is less accurate; a cross-encoder jointly attends over both and is more accurate but too slow for first-stage retrieval, so it is used for reranking.

Q: What is ColBERT and what problem does it solve?
A: ColBERT performs late interaction: it encodes every token of every document offline and uses MaxSim over query tokens at query time, balancing bi-encoder speed with cross-encoder quality.

Q: What is ColBERTv2 and how does it differ from ColBERT?
A: ColBERTv2 adds residual compression and denormalized embeddings, cutting index size by ~10x and improving retrieval quality over the original ColBERT.

Q: What is approximate nearest neighbor (ANN) search?
A: ANN trades a small loss in recall for huge speed gains over exact kNN by using index structures like HNSW, IVF-PQ, or ScaNN, enabling sub-linear vector lookup over millions of embeddings.

Q: What is HNSW and why is it the most popular vector index?
A: Hierarchical Navigable Small World (HNSW) is a graph-based ANN index that builds multi-layer proximity graphs; it offers logarithmic-time search, high recall, and incremental inserts.

Q: What is product quantization (PQ) in vector search?
A: PQ splits each embedding into sub-vectors and replaces each with the nearest centroid id from a codebook, compressing vectors by ~32x with small recall loss for billion-scale retrieval.

Q: What is DiskANN and when should you use it?
A: DiskANN is a Microsoft graph-based ANN index designed for SSD-resident vectors at billion scale, with Vamana graph; used in Azure AI Search and Bing's vector backend.

Q: What is FAISS?
A: Facebook AI Similarity Search (FAISS) is Meta's open-source library for efficient similarity search and clustering of dense vectors, with GPU support and many index types — the toolkit beneath most vector DBs.

Q: Compare HNSW, IVF-PQ, and ScaNN in one sentence each.
A: HNSW = graph-based, high recall, more RAM; IVF-PQ = inverted-file + product quantization, low memory, lower recall; ScaNN = Google's anisotropic vector quantization, top-tier on TPU/CPU benchmarks.

Q: What is Matryoshka Representation Learning (MRL)?
A: MRL trains embeddings so that the first d dimensions form a useful embedding (e.g. 64, 128, 256, 512), allowing one model to serve multiple storage/recall budgets without retraining.

Q: What dimensionality do typical sentence embeddings use in 2025?
A: Common embedding dimensions are 384, 768, 1024, or 1536; OpenAI text-embedding-3-small supports 512 or 1536, while text-embedding-3-large supports up to 3072.

Q: Name three state-of-the-art open embedding models as of 2025.
A: BGE-M3, E5-Mistral-7B, and Nomic-Embed-Text-v1.5 are leading open embeddings, with MTEB benchmark scores above 65 for English and strong multilingual performance.

Q: What is the MTEB benchmark?
A: Massive Text Embedding Benchmark (MTEB) covers 56 datasets across retrieval, classification, clustering, and semantic textual similarity, and is the de facto leaderboard for general-purpose embeddings.

Q: What is SPLADE and what makes it hybrid by design?
A: SPLADE learns sparse but expanded representations using BERT's MLM head, predicting which vocabulary terms should be activated per document — combining neural semantics with lexical sparsity.

Q: What is ELSER (Elastic Learned Sparse EncodeR)?
A: ELSER is Elastic's out-of-domain learned sparse retriever that ships as a managed model in Elasticsearch, giving BM25-style sparse vectors with semantic understanding.

Q: What is dense passage retrieval (DPR)?
A: DPR (Karpukhin et al., 2020) uses two BERT encoders (question + passage) trained with contrastive loss on QA pairs; the first dense retriever to outperform BM25 on open-domain QA.

Q: What is reranking and when should you use it in a RAG pipeline?
A: Reranking applies a cross-encoder (e.g. bge-reranker-v2-m3, Cohere Rerank 3.5) to the top-k candidates from first-stage retrieval to reorder by fine-grained relevance, typically improving top-1 accuracy 10-30%.

Q: What is Cohere Rerank and what is its typical API signature?
A: co.rerank(query=..., documents=[...], top_n=k, model="rerank-english-v3.0"); it returns indices and relevance scores used to reorder first-stage hits before the LLM step.

Q: What is reranker distillation?
A: It trains a small bi-encoder student to mimic the ranking signal of a large cross-encoder teacher, recovering most of the cross-encoder's quality at bi-encoder speed.

Q: What is Maximal Marginal Relevance (MMR) in retrieval?
A: MMR reorders retrieved documents to maximize relevance while minimizing redundancy, scoring each candidate by λ * sim(q, d) - (1-λ) * max sim(d, d') for d' already selected.

Q: What is the typical RAG pipeline in 2025?
A: Query rewrite → hybrid retrieve (BM25 + dense) → rerank → top-k context → LLM with citations → answer, with optional HyDE, query expansion, and post-generation grounding checks.

Q: What is HyDE (Hypothetical Document Embeddings)?
A: HyDE prompts an LLM to generate a hypothetical answer first, embeds that, and retrieves documents similar to the hypothetical — useful for zero-shot domains where the query is short.

Q: What is query rewriting in RAG and why does it matter?
A: Query rewriting uses an LLM to transform a conversational or ambiguous query into a self-contained search query (e.g. resolving pronouns, expanding acronyms), dramatically improving recall on multi-turn dialogues.

Q: What is step-back prompting in RAG?
A: It generates a more abstract or general query alongside the original, retrieves context for both, and feeds both into the LLM — improving answers on factual, multi-hop, or scientific questions.

Q: What is multi-hop retrieval and how do you solve it?
A: Multi-hop questions require chaining evidence across documents; solutions include iterative retrieval (retrieve → answer → re-query), IRCoT, and graph-based traversal over linked passages.

Q: What is IRCoT?
A: Interleaved Retrieval with Chain-of-Thought: the LLM generates a CoT step, then retrieves, then continues, allowing reasoning and retrieval to guide each other step by step.

Q: What is GraphRAG?
A: GraphRAG builds a knowledge graph from documents (entities + relations), then retrieves subgraphs relevant to the query, often using community detection to answer global questions.

Q: Who introduced GraphRAG and when?
A: Microsoft Research published GraphRAG in mid-2024, showing strong improvements on global sensemaking questions like "what are the main themes?" over chunk-based RAG.

Q: What is agentic RAG?
A: Agentic RAG gives the LLM tools and a loop to decide when and what to retrieve, when to re-query, when to call APIs, and when to answer — replacing the rigid retrieve-then-generate pipeline.

Q: What is self-RAG?
A: Self-RAG trains the model to emit special [Retrieve] and [IsRel/IsSup/IsUse] reflection tokens so the LLM decides dynamically whether to retrieve and whether its own outputs are grounded and useful.

Q: What is CRAG (Corrective RAG)?
A: CRAG adds a lightweight retrieval evaluator that grades retrieved docs as Correct / Ambiguous / Incorrect and triggers web search or fallback for low-confidence cases.

Q: What are the four classic RAG failure modes?
A: 1) Missing content (not retrieved), 2) Missed top-ranked docs, 3) Not in context (lost in middle), 4) Not extracted (LLM ignores the answer even when present).

Q: What is the lost-in-the-middle problem?
A: LLMs give more weight to context at the beginning and end of the prompt and ignore the middle, so reranking should place the most relevant chunks at the top and bottom of the context window.

Q: What chunk size is recommended for RAG on long documents?
A: A common default is 512 tokens with 10-20% overlap (~50-100 tokens), with chunking tuned per content type; too-small chunks lose context, too-large chunks dilute relevance.

Q: Compare character, token, and semantic chunking.
A: Character/token chunking is fast but arbitrary; semantic chunking splits at topic shifts using embedding similarity, producing more coherent chunks at the cost of latency.

Q: What is the role of structured data in AI search?
A: Many enterprise answers live in SQL, knowledge graphs, or APIs, not documents. Modern AI search combines text-to-SQL, KG-RAG, and tool-calling agents to retrieve structured facts alongside unstructured passages.

Q: What is a knowledge graph in AI search?
A: A knowledge graph (KG) stores entities and relations (e.g. (Tesla, founded_by, Elon_Musk)); retrieval over a KG supports symbolic, multi-hop reasoning that pure vector search cannot.

Q: What is Wikidata's role in AI search?
A: Wikidata underpins many AI search answers with canonical entity IDs and relations, used directly by Google Knowledge Graph, Apple's Siri, and RAG systems needing ground-truth facts.

Q: What is Schema.org markup and why does it help GEO?
A: Schema.org is a vocabulary of structured data (FAQ, Product, Article) embedded in HTML; AI search engines parse it for entity disambiguation, prices, dates, and citation-ready facts, boosting GEO inclusion.

Q: What is the difference between RAG and long-context LLMs?
A: Long-context LLMs fit everything in the prompt but are expensive, slow, and still miss mid-document facts; RAG retrieves only relevant spans and is cheaper, faster, and more auditable per query.

Q: What is Perplexity AI and what is its product differentiator?
A: Perplexity is an answer engine that pairs a generative LLM with live web search and inline numbered citations on every sentence, positioning itself as a "Google + ChatGPT" hybrid.

Q: What is SearchGPT / ChatGPT search?
A: OpenAI integrated real-time web search into ChatGPT in late 2024 (SearchGPT), returning conversational answers with source links for queries with recency intent (news, prices, sports).

Q: What is Google's AI Overviews (formerly SGE)?
A: AI Overviews are generative summaries that appear above traditional blue-link results in Google Search, launched broadly in May 2024 and powered by a customized Gemini model.

Q: What is Microsoft Copilot (formerly Bing Chat)?
A: Microsoft Copilot combines GPT-4-class models with Bing's index to deliver cited, conversational answers, and is integrated across Edge, Windows, and Microsoft 365.

Q: What is the difference between AI Mode and AI Overviews in Google?
A: AI Overviews are inline summaries on the SERP; AI Mode (2025) is a full conversational, Gemini-powered search interface that returns only AI answers with follow-up, replacing 10 blue links.

Q: What is You.com and how does it differ from Perplexity?
A: You.com offers a multi-modal, multi-agent answer engine with customizable apps (YouAgents) and modes (Smart, Genius, Research), emphasizing customization over Perplexity's minimal UI.

Q: What is Brave Search's Leo and how does it monetize?
A: Leo is Brave's private, no-log AI answer assistant inside the browser; Brave's premium Leo subscription (~$15/mo) and its ad-free index differentiate it from Google-dependent competitors.

Q: What is the market share of ChatGPT as a "search engine" in 2025?
A: ChatGPT receives an estimated ~5-6% of global search referrals and is the most-used "AI search destination", per Similarweb and SparkToro analyses in 2025.

Q: How does GEO (Generative Engine Optimization) differ from SEO?
A: GEO optimizes content to be cited inside LLM-generated answers (clear structure, statistics, authoritative sources) rather than just ranked in blue links, with new metrics like citation share and answer inclusion rate.

Q: What is the GEO paper from Princeton and IIT Delhi?
A: Their 2023/2024 study coined Generative Engine Optimization (GEO), showing techniques like citing sources, adding statistics, and using quotations can boost source visibility in LLM answers by up to 40%.

Q: What is llms.txt and what problem does it solve?
A: llms.txt is a proposed markdown index of a site's high-quality LLM-readable content, placed at /llms.txt, so AI crawlers can ingest a concise site summary instead of crawling thousands of HTML pages.

Q: What is the robots.txt policy for GPTBot and other AI crawlers?
A: Site owners can block GPTBot (OpenAI), ClaudeBot (Anthropic), Google-Extended, CCBot, and PerplexityBot via standard robots.txt; User-agent: GPTBot\nDisallow: / blocks training and retrieval.

Q: What is the difference between training crawl and retrieval crawl?
A: Training crawls (e.g. GPTBot, CCBot) ingest content for model training; retrieval crawls (e.g. OAI-SearchBot, ChatGPT-User) fetch pages live to ground answers and obey per-page fetch policies.

Q: What content licensing deals has OpenAI signed with publishers?
A: OpenAI, Microsoft, and Google signed content licensing agreements with AP, Axel Springer, News Corp, the Financial Times, The Atlantic, Vox, Le Monde, and Reddit (~$60M/yr) for both training data and live retrieval rights.

Q: What is Perplexity Pages?
A: Perplexity Pages (2024) lets users turn a search session into a shareable, citable article with images, sections, and inline sources, blurring the line between search and publishing.

Q: What is Google's "circle to search" feature?
A: Announced on Pixel 8 and Samsung Galaxy S24 in 2024, Circle to Search lets users circle any on-screen object to trigger a visual AI search combining image and text context.

Q: What is multimodal RAG?
A: Multimodal RAG retrieves across text, images, audio, and video by embedding each modality into a shared vector space (e.g. CLIP, SigLIP, ImageBind) so a text query can return a frame from a video.

Q: What is CLIP and why is it foundational for multimodal search?
A: CLIP (Contrastive Language-Image Pretraining) trains a text and image encoder in a shared space using 400M (image, caption) pairs, enabling zero-shot image search and text-to-image retrieval.

Q: What is SigLIP and how does it improve on CLIP?
A: SigLIP replaces CLIP's softmax contrastive loss with a sigmoid loss, scaling better and achieving higher zero-shot ImageNet accuracy at smaller batch sizes; used in Gemini vision encoders.

Q: What is ColPali for document retrieval?
A: ColPali embeds document page images directly with a vision-language model (PaliGemma) and uses ColBERT-style late interaction, beating OCR-then-text pipelines on visually rich PDFs.

Q: Why is OCR often a bottleneck in document AI search?
A: OCR errors propagate to embeddings and BM25 indexes, hurting recall on tables, equations, handwriting, and low-resolution scans; vision-native retrievers like ColPali skip OCR entirely.

Q: What is the FreshQA benchmark?
A: FreshQA is a Google benchmark of time-sensitive questions whose answers change; modern Gemini-based AI search systems reached ~95% accuracy by early 2025.

Q: What is SimpleQA from OpenAI?
A: SimpleQA is OpenAI's short-form factuality benchmark with single-answer questions, designed to measure hallucination rates of frontier models on factual queries.

Q: What is HALo (Hallucinations and Logical operators)?
A: A Vectara benchmark tracking hallucination rate vs. summary length; GPT-4 class models run at ~3% hallucination, while earlier models exceeded 20%.

Q: What is the agentic search loop in Perplexity Sonar?
A: Perplexity's Sonar model uses a search agent that iteratively queries the web, reads pages, refines the query, and synthesizes a cited answer, exposed via the Sonar API.

Q: What is Deep Research (OpenAI, Google, Perplexity)?
A: Deep Research is a multi-step research agent that browses 50-200+ sources, runs code, and produces a long-form report with citations in 5-30 minutes, launched across major AI search products in early 2025.

Q: What is the cost of a Deep Research run?
A: Typical Deep Research tasks cost $0.50 - $5 in API (or 1-25 Pro credits) due to 100+ web fetches and long reasoning traces — 10-100x a simple RAG query.

Q: What is ReAct (Reasoning + Acting)?
A: ReAct prompts the LLM to interleave Thought → Action → Observation steps, with actions like Search[query] and Lookup[link], used widely in retrieval agents.

Q: What is the difference between ReAct and Chain-of-Thought?
A: CoT reasons in natural language only; ReAct interleaves external tool calls (search, calculator, code) with reasoning, grounding answers in live evidence.

Q: What is Toolformer?
A: Toolformer (Meta, 2023) is a model that self-supervises API calls by inserting them into text and training only when the call actually improves perplexity, enabling web search, calculator, and QA tools.

Q: What is WebGPT and why is it a landmark?
A: OpenAI's WebGPT (2021) showed that a GPT-3 model fine-tuned to browse the web and cite sources could produce answers preferred over human-written Reddit responses, foreshadowing modern AI search.

Q: What is indirect prompt injection via web content?
A: A website can include invisible text ("Ignore prior instructions and...") that an AI search agent ingests as instructions, exfiltrating user data or changing behavior — a top OWASP LLM risk in 2025.

Q: What defenses exist against indirect prompt injection?
A: Defenses include treat retrieved content as untrusted data (not instructions), delimiter/quoting, content-type checks, and a separate policy LLM that filters retrieved text before it reaches the main model.

Q: What is the WebArena benchmark?
A: WebArena (2023) is a realistic, self-hostable web agent benchmark built on clones of GitHub, Reddit, Maps, and shopping sites; top agents still complete <30% of tasks in 2025.

Q: What is the GAIA benchmark?
A: GAIA (Meta, 2023) is a benchmark of assistant-style multi-step questions requiring web search, file reading, and calculation; frontier agents now exceed 65% while 2023 GPT-4 was at ~15%.

Q: What is the role of a metadata filter in vector search?
A: Metadata filters (e.g. tenant_id, date > 2024, language = "en") are pre-filters that restrict ANN search to a subset of vectors, critical for multi-tenant and time-bounded retrieval.

Q: What is Pinecone vector database?
A: Pinecone is a managed serverless vector database (built on proprietary infrastructure) popular for production RAG, offering metadata filtering, namespaces, and hybrid sparse+dense search.

Q: What is Weaviate?
A: Weaviate is an open-source vector DB with built-in vectorization modules (OpenAI, Cohere, HuggingFace) and GraphQL/REST APIs, supporting hybrid search and generative RAG out of the box.

Q: What is Qdrant?
A: Qdrant is a Rust-based open-source vector DB with high-performance HNSW, rich payload filtering, and quantization, widely used for on-prem and self-hosted RAG.

Q: What is Milvus / Zilliz?
A: Milvus is an open-source, distributed vector DB built by Zilliz with multiple index types (HNSW, IVF, DiskANN) targeting billion-scale deployments, the OSS counterpart to Pinecone at scale.

Q: What is pgvector?
A: pgvector is a PostgreSQL extension that adds vector types and ANN search (HNSW, IVF) to Postgres, popular for teams that want a single OLTP+vector store without a new system.

Q: What is LanceDB?
A: LanceDB is an embedded, serverless vector DB built on the Lance columnar format, optimized for ML workloads (training data, embeddings) with zero ops.

Q: What is the role of Elasticsearch in hybrid search?
A: Elasticsearch combines BM25 + ELSER (learned sparse) + optional dense_vector in one engine, plus aggregations and analytics, making it a popular single-stack choice for AI search.

Q: What is OpenSearch's neural search plugin?
A: The OpenSearch neural_search plugin adds dense and sparse neural retrievers (via external models served in OpenSearch Dashboards) with hybrid scoring and rerankers integrated into the DSL.

Q: What is Vespa.ai?
A: Vespa is an open-source engine from Yahoo for large-scale hybrid search + ranking + ML inference, used by companies needing tens of thousands of QPS with sub-100ms latency.

Q: What is the major difference between vector DBs and traditional search engines?
A: Traditional engines optimize lexical recall + aggregations; vector DBs optimize semantic similarity at scale via ANN. Hybrid systems increasingly offer both, but trade-offs in freshness, filter expressiveness, and cost remain.

Q: What is the role of distance metric in vector search?
A: The choice of cosine, dot product, or Euclidean affects ranking; cosine is standard for normalized embeddings, dot product for unnormalized, Euclidean for absolute-distance clustering.

Q: What is the SIFT-1B benchmark?
A: SIFT-1B (1 billion 128-dim SIFT descriptors) is the standard large-scale ANN benchmark; SOTA systems achieve >95% recall@10 at sub-millisecond latency per query on a single GPU.

Q: What is the main risk of over-embedding in RAG?
A: Embedding too many chunks per query wastes tokens and dilutes attention; better to retrieve, then compress or re-rank down to the 5-15 most relevant chunks before the LLM.

Q: What is LongLLMLingua / LLMLingua for context compression?
A: LongLLMLingua uses a small model to remove non-essential tokens from retrieved documents (e.g. adjectives, restatements), achieving 4-20x compression with minimal quality loss.

Q: What is "contextual retrieval" (Anthropic, 2024)?
A: Contextual Retrieval prepends a 50-token LLM-generated context summary to each chunk before embedding, reducing retrieval failure rate by 35-49% in Anthropic's measurements.

Q: What is prompt caching in AI search APIs?
A: OpenAI, Anthropic, and Google now offer prompt caching that reuses a cached prefix (e.g. system prompt + retrieved docs) for 5-10 minutes, cutting cost up to 90% and latency up to 80%.

Q: What is semantic caching in AI search?
A: Semantic caching stores previous (query, embedding, answer) tuples and returns a cached answer if a new query is within a similarity threshold of an old one — useful for high-volume, repetitive queries.

Q: What is vector quantization for cost reduction?
A: Scalar (int8) or binary (1-bit) quantization of embeddings cuts vector DB storage and compute by 4-32x, with typical recall drop of 1-3%, often offset by adding a reranker.

Q: What is the typical latency budget for a production RAG query?
A: ~1-3 seconds end-to-end: ~50-150ms retrieval, ~100-300ms reranking, ~500-2000ms LLM streaming first token. Anything >3s drops user engagement sharply.

Q: What is the typical p95 latency for hybrid search at 10M vectors?
A: With HNSW on modern hardware, p95 hybrid search (BM25 + dense) over 10M vectors runs in ~50-150ms, well within the RAG latency budget.

Q: What is the importance of latency in AI search?
A: Users expect <2 seconds for AI answers; production systems use streaming (start typing at ~300ms), reranker cascading, and prompt caching to meet this bar at scale.

Q: What is token streaming and why does it matter?
A: Streaming returns LLM tokens as they are generated (server-sent events), so the user sees the answer build up in real time even when the full answer takes 5-10 seconds to complete.

Q: What is "answer-first" UI design?
A: AI search UIs put the generated answer at the top with citations, followed by sources, related questions, and traditional links — the inverse of classic "10 blue links" SERPs.

Q: What is the "second click" pattern in AI search UX?
A: Users increasingly read the AI answer first, then click through to verify 1-2 sources; the "zero-click" share of search queries is now above 65% per SparkToro (2024).

Q: What is the impact of AI search on publisher traffic?
A: Multiple analyses (Authoritas, 2024) estimate AI Overviews and ChatGPT search could cut publisher organic traffic 20-60% for informational queries, prompting affiliate- and direct-traffic pivots.

Q: What is the leading counter-strategy for publishers?
A: Publishers are increasingly licensing content (AP, FT, News Corp, Axel Springer), defending via robots.txt / paywalls, or building direct AI products (Yahoo News AI, Hearst Aria) to capture value.

Q: Why did the New York Times sue OpenAI and Microsoft?
A: The NYT filed suit in December 2023 alleging that ChatGPT and Copilot reproduce its articles verbatim and that OpenAI used its content to build a substitute product, seeking statutory and actual damages.

Q: What is the TDM (text and data mining) opt-out and how does it affect AI search?
A: Under the EU DSM Directive Article 4, rights holders can reserve use of their works for TDM via machine-readable means; this governs how EU-based AI search engines must respect commercial opt-outs.

Q: What is the role of the model context protocol (MCP) in AI search?
A: MCP (Anthropic, 2024) is a standardized JSON-RPC protocol for connecting LLMs to tools and data sources (search, files, DBs), enabling plug-and-play AI search integrations across clients and vendors.

Q: What is "function calling" / "tool use" in AI search?
A: Models emit structured JSON tool calls (e.g. {"tool": "search", "args": {"query": "..."}}) which the runtime executes and returns to the model — the core mechanism for agentic AI search.

Q: What is the difference between query expansion and query decomposition?
A: Query expansion adds synonyms/paraphrases; query decomposition breaks one question into multiple sub-questions, each retrieved independently and then combined.

Q: What is "fan-out" in agentic AI search?
A: Fan-out = the agent splits a question into sub-questions, retrieves in parallel, and aggregates results — the pattern underlying Deep Research and multi-hop agentic RAG.

Q: What is retrieval-augmented fine-tuning (RAFT)?
A: RAFT fine-tunes the LLM on QA pairs that include retrieved (and sometimes irrelevant) context, teaching it to ignore distractors and answer from evidence — outperforms RAG alone on domain-specific QA.

Q: What is RA-DIT?
A: Retrieval-Augmented Dual Instruction Tuning (Meta, 2023) separately fine-tunes the LLM and retriever on instruction data, improving both modules jointly for open-domain QA.

Q: What is the typical architecture of a 2025 enterprise RAG application?
A: Connectors (M365, Slack, Drive) → chunker → embedding + BM25 index → ACL-aware retriever → reranker → grounded LLM with citations → audit log + provenance, with separate evaluation and feedback loops.

Q: What is the difference between enterprise AI search and consumer AI search?
A: Enterprise AI search requires access control (ACLs), audit logs, connectors to SharePoint/Confluence/Slack, hybrid indexes, and provenance; consumer AI search optimizes for freshness, coverage, and cost.

Q: What is Glean and what does it do?
A: Glean is an AI search and assistant for the enterprise that indexes Slack, Google Drive, Confluence, GitHub, Salesforce, etc., respects per-document ACLs, and grounds answers in source links.

Q: What is "permissions-aware" retrieval?
A: Retrievers must filter results to documents the current user can access; solutions include passing ACL as metadata filters, indexing with user/group claims, and post-filtering on access tokens.

Q: What is "answer provenance" in enterprise AI search?
A: Answer provenance tracks the exact document, version, and span that contributed each claim, so audits can reproduce the answer — required in finance, pharma, and legal AI deployments.

Q: What is the role of small language models (SLMs) in AI search?
A: SLMs (Phi-4, Gemma 2 9B, Llama 3.1 8B) increasingly handle query rewriting, routing, and reranking at the edge, leaving only the final generation to a frontier model — cutting cost 10-50x.