AI Quick Reference

Looking for fast answers or a quick refresher on AI-related topics? The AI Quick Reference has everything you need—straightforward explanations, practical solutions, and insights on the latest trends like LLMs, vector databases, RAG, and more to supercharge your AI projects!

• How might librarians or information specialists make use of DeepResearch in their work for retrieving information?
• What best practices can organizations adopt to make the most efficient use of DeepResearch given limits like query caps (e.g., 100 queries per month)?
• What is the "Humanity's Last Exam" benchmark and how did DeepResearch perform on it compared to other AI models?
• How does the complexity of a query affect DeepResearch's performance or the level of detail in its output?
• How does the cost or accessibility of DeepResearch affect who can use it and for what purposes?
• What is the significance of DeepResearch being described as an "AI agent" rather than just a chatbot?
• What are the ethical considerations of using an AI like DeepResearch for research?
• Why might the tone or style of DeepResearch's report not meet your needs or expectations, and is there a way to adjust it?
• How do you access DeepResearch in ChatGPT, and are there any prerequisites or settings to enable it?
• How could DeepResearch be combined with data analysis tools for a thorough research project (e.g., first gather info then analyze statistics)?
• How can you specify or adjust the amount of time DeepResearch spends on a query, if at all?
• How do you know if DeepResearch has used outdated information, and what can you do to verify the timeliness of its data?
• How can a user identify if DeepResearch might have missed something important in its report, and what steps can be taken next?
• How can users effectively simplify or break down a query to fit within DeepResearch's capabilities if needed?
• How can you ensure that DeepResearch covers all necessary aspects of a topic in its output (do you need to break the query into parts)?
• What strategies can DeepResearch users employ to ensure it doesn't go down irrelevant paths during its research process?
• How might one gauge the completeness of DeepResearch's research on a topic (for example, knowing if it covered most relevant information)?
• What are best practices for phrasing your query to get the most relevant results from DeepResearch?
• How can one improve the relevance or quality of DeepResearch's output if the initial results are not satisfactory?
• How do you include a document (such as a PDF) or image for DeepResearch to analyze as part of its research?
• How can you incorporate DeepResearch results into your work (for example, exporting or sharing the generated report)?
• What are the steps to initiate a DeepResearch query and what happens after you submit a query?
• How might one measure the efficiency of using DeepResearch (for example, the amount of useful information obtained per query)?
• How can you refine a query if DeepResearch returns a report that is too broad or, conversely, too narrow in scope?
• How can you use DeepResearch to research a very niche or highly technical topic effectively?
• How do you resolve issues where DeepResearch stops before the allotted time and provides a short answer instead of a detailed report?
• How can you troubleshoot queries that consistently cause DeepResearch to crash, hang, or otherwise fail?
• How can you troubleshoot if DeepResearch returns an error or fails to produce a report altogether?
• How can you verify the accuracy of a figure or statistic given by DeepResearch in its report?
• What are common mistakes users make when formulating queries for DeepResearch that could lead to poor results?
• What does it mean if DeepResearch says it cannot find enough information on a given topic, and how should you respond?
• What does it mean that DeepResearch operates autonomously for 5 to 30 minutes on a query?
• How do you correct or refine a DeepResearch query if the initial results are not on target?
• How should you handle a situation where DeepResearch's cited sources are behind paywalls or otherwise inaccessible to you?
• Can DeepResearch assist in patent research or exploring prior art when innovating a new product?
• Can DeepResearch be directed to focus on certain subtopics or questions within a broad research topic?
• Can DeepResearch be used in scientific research to gather data and references on a hypothesis?
• Can DeepResearch be used effectively on a mobile device or a slower internet connection, or would that impact its performance?
• Are there differences in DeepResearch's output quality when it uses the full time budget (e.g., 30 minutes) versus a shorter duration?
• Is it possible to get intermediate updates or see what DeepResearch is doing during its research process?
• Are there ways to evaluate the quality of DeepResearch's citations or the reliability of its sources to ensure high-quality results?
• Can a user do anything to help DeepResearch process information faster, such as providing initial context or reference links?
• Can you chain multiple DeepResearch queries together to explore different angles or subtopics of a larger topic?
• What is the "O3" model mentioned in connection with DeepResearch, and how does it relate to GPT-4 or other models?
• How does DeepResearch manage to generate a comprehensive report instead of just a single answer?
• What is the historical context or motivation behind developing DeepResearch (why did OpenAI create it)?
• What kind of user input does DeepResearch require and how does a user initiate a DeepResearch session?
• What distinguishes DeepResearch's output format from a typical search engine results page?
• Can users influence which sources DeepResearch uses or provide it with specific starting points for research?
• What is the maximum length or complexity of a question or prompt you can give to DeepResearch?
• How do you know when DeepResearch has finished its research and is ready to present the results?
• How can you verify or follow up on the sources that DeepResearch cites in its report?
• How would you use DeepResearch to perform a comparative analysis of two different concepts or technologies?
• Is it possible to integrate DeepResearch with external tools (like note-taking apps or knowledge bases)?
• Can DeepResearch be used for tasks like literature reviews or academic research, and if so, how?
• How might a business analyst or market researcher utilize DeepResearch for competitive analysis?
• How can DeepResearch be applied in legal research or the analysis of case law and statutes?
• In what ways might healthcare professionals use DeepResearch to find up-to-date medical information or literature?
• Can DeepResearch be used to generate concise summaries of complex topics for quick understanding in a business setting?
• How can DeepResearch be used for learning about new skills or topics (for example, getting an overview of a new programming language or technology)?
• What are the advantages of using DeepResearch for gathering multiple perspectives on a controversial topic?
• What factors influence how long DeepResearch takes to complete a research query?
• In what ways can users optimize their queries to reduce the time DeepResearch needs to find information?
• What is the typical length or detail of a report generated by DeepResearch, and can this be adjusted or controlled?
• How does the version or updates of DeepResearch (or its underlying model) impact its performance or capabilities over time?
• What happens if DeepResearch's maximum research time (e.g., 30 minutes) is not enough to cover a particularly complex query?
• How does DeepResearch choose between exploring many sources broadly vs. diving deep into a few, and can this strategy be influenced for better results?
• How does DeepResearch balance speed and thoroughness when gathering and synthesizing information?
• What are the system or resource limitations (if any) that affect DeepResearch's performance or the size of data it can handle?
• What steps can you take if DeepResearch's output is off-topic or not directly addressing your question?
• If DeepResearch is not currently available in your country or region, what alternatives or solutions might you consider?
• How can a user report or provide feedback on incorrect results or bugs encountered while using DeepResearch?
• What could cause differences in DeepResearch's output when asking similar questions at different times?
• How can you handle situations where DeepResearch's answer seems plagiarized or too closely paraphrased from a single source?
• What is multimodal search and how does it differ from traditional search?
• What is multimodal RAG (Retrieval-Augmented Generation) and how does it work?
• What are the key components of a multimodal search system?
• How do multimodal embeddings capture both visual and textual information?
• What are the advantages of multimodal search over single-modality approaches?
• How do multimodal systems handle the semantic gap between different modalities?
• What is cross-modal retrieval and how does it differ from multimodal search?
• What are the most common modalities used in multimodal search systems?
• How does multimodal RAG extend traditional text-based RAG systems?
• What are the real-world applications of multimodal search and RAG?
• What are vision-language models (VLMs) and how are they used in multimodal search?
• How does CLIP (Contrastive Language-Image Pre-training) work for multimodal embeddings?
• What are the advantages of using CLIP for multimodal search?
• What are the alternatives to CLIP for multimodal embeddings?
• How do Florence, ALIGN, and other multimodal models compare to CLIP?
• What is the difference between image captioning and multimodal embedding approaches?
• How are audio embeddings integrated into multimodal search systems?
• What models work best for video understanding in multimodal search?
• How do unified multimodal models like FLAVA or ImageBind work?
• What are instruction-tuned multimodal models and how do they improve search?
• What is the typical architecture for a multimodal search system?
• How do you design a multimodal vector database?
• What are the challenges in aligning embeddings from different modalities?
• How do you handle different embedding dimensions across modalities?
• What are the best practices for joint vs. separate indexes for different modalities?