AI search increasingly operates across modalities and languages, not just text. Multimodal AI search can understand and retrieve information across text, images, audio, and video formats. CLIP (Contrastive Language-Image Pretraining) trained a text and image encoder in a shared space using 400 million image-caption pairs, enabling zero-shot image search and text-to-image retrieval. SigLIP replaces CLIP's softmax contrastive loss with a sigmoid loss, scaling better and achieving higher zero-shot ImageNet accuracy at smaller batch sizes — it is now used in Gemini vision encoders. 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. Vision-native retrievers like ColPali are particularly valuable because OCR errors propagate to embeddings and BM25 indexes, hurting recall on tables, equations, handwriting, and low-resolution scans.
Beyond multimodality, AI search handles a wide range of specialized challenges. Cross-lingual information retrieval lets AI systems search and retrieve information across multiple languages, understanding queries in one language and finding relevant content in others. Zero-shot and few-shot learning allow search systems to handle new types of queries without extensive retraining, adapting quickly to new domains and languages. When faced with ambiguous queries, AI search engines use context clues, clarification questions, and multiple intent detection to provide relevant results or ask for clarification when needed. Real-time information is accessed through External Information Retrieval, which pulls current data from the web, APIs, and live sources beyond training data. AI search engines improve personalization by learning from user behavior, context, and preferences, and they rely on conversation history, user preferences, and search context to deliver more relevant results over time. Advanced systems provide direct citations and links to original sources, allowing users to verify information and explore further, and combat misinformation through source verification, citation linking, confidence scoring, and retrieval from authoritative sources.
A growing ecosystem of benchmarks tracks these capabilities. FreshQA, a Google benchmark of time-sensitive questions whose answers change, found that modern Gemini-based AI search systems reached roughly 95% accuracy by early 2025. OpenAI's SimpleQA is a short-form factuality benchmark with single-answer questions designed to measure hallucination rates. HALo (Hallucinations and Logical operators) from Vectara tracks hallucination rate vs. summary length; GPT-4 class models run at about 3% hallucination, while earlier models exceeded 20%. For agentic systems, 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 sat at roughly 15%. WebArena (2023) is a realistic, self-hostable web agent benchmark built on clones of GitHub, Reddit, Maps, and shopping sites; top agents still complete fewer than 30% of tasks in 2025.
These capabilities are increasingly delivered through deep research agents. Deep Research is a multi-step research agent from OpenAI, Google, and Perplexity that browses 50-200+ sources, runs code, and produces a long-form report with citations in 5-30 minutes. Typical Deep Research tasks cost $0.50 to $5 in API (or 1-25 Pro credits) due to the 100+ web fetches and long reasoning traces — roughly 10-100x a simple RAG query. Perplexity's Sonar model exposes an agentic search loop via API that iteratively queries the web, reads pages, refines the query, and synthesizes a cited answer. However, these agents face real security risks: indirect prompt injection via web content occurs when a website includes invisible text instructing the AI to ignore its priors, exfiltrate user data, or change behavior — a top OWASP LLM risk in 2025. Defenses include treating retrieved content as untrusted data rather than instructions, using delimiters and quoting, performing content-type checks, and routing retrieved text through a separate policy LLM before it reaches the main model.