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 do prompts in Model Context Protocol (MCP) shape model behavior?
- What is the relationship between system prompts and exposed prompts?
- Can LLMs misuse tools if not properly structured?
- What techniques reduce hallucination in tool use?
- How do I provide clear fallback behavior for failed tool calls?
- Can models chain tools together with Model Context Protocol (MCP)?
- How does an LLM handle ambiguous or multi-purpose tools?
- What are best practices for making tool inputs model-friendly?
- Can I connect Model Context Protocol (MCP) servers to databases or file systems?
- How does Model Context Protocol (MCP) fit into Retrieval-Augmented Generation (RAG) workflows?
- Can I build an AI assistant for developers using Model Context Protocol (MCP)?
- How can I connect Model Context Protocol (MCP) to my company’s internal APIs?
- Can I use Model Context Protocol (MCP) with desktop or browser-based apps?
- How does Model Context Protocol (MCP) interact with Claude Desktop or other host apps?
- Can I integrate Model Context Protocol (MCP) with customer support systems or CRMs?
- Is Model Context Protocol (MCP) a good fit for multi-agent LLM systems?
- How can I connect multiple Model Context Protocol (MCP) servers to the same LLM?
- How can I build a modular plugin ecosystem using Model Context Protocol (MCP)?
- Where is the Model Context Protocol (MCP) spec maintained and how often is it updated?
- How do I contribute to the Model Context Protocol (MCP) spec or ecosystem?
- Are there known community projects or examples I can follow?
- How do I build reusable Model Context Protocol (MCP) modules or packages?
- What are some notable open-source Model Context Protocol (MCP) servers?
- How can teams collaborate on Model Context Protocol (MCP) server development?
- Are there planned features or roadmap items for Model Context Protocol (MCP)?
- How is Anthropic supporting or evolving the Model Context Protocol (MCP) spec?
- What are common mistakes developers make when first using Model Context Protocol (MCP)?
- What does the future of AI development look like with Model Context Protocol (MCP) as a standard?
- What is a vector database and how does it apply to video surveillance?
- How are video frames or footage represented as vectors?
- Why are traditional relational databases insufficient for video surveillance?
- What are embeddings in the context of surveillance footage?
- How does a vector database enable real-time search in video systems?
- What types of surveillance data can be stored as vectors?
- How does object detection work with vector representations?
- What’s the difference between indexing frames and indexing events?
- What are the benefits of using approximate nearest neighbor (ANN) search in surveillance?
- How do you structure a video analytics pipeline with a vector database?
- How do you convert raw video into searchable vectors?
- What are best practices for frame sampling and selection?
- What preprocessing steps are required before vectorization?
- How do you detect and extract objects (people, vehicles, etc.) from video feeds?
- How do you generate embeddings from face or body features?
- Which models are best for generating video embeddings?
- How do you ensure consistent vector representations over time?
- What role does metadata (timestamp, camera location) play in ingestion?
- How do you batch process historical video archives into a vector DB?
- Can you ingest live video streams into a vector database?
- How does similarity search work for surveillance footage?
- How can you search for a person seen across multiple cameras?
- What types of vector search methods are suitable for video surveillance?
- How does filtering by time or camera ID work in combination with vector search?
- Can you use multimodal queries (e.g., vector + metadata)?
- What is hybrid search and how can it improve surveillance investigations?
- How do you evaluate the quality of vector search results?
- What is vector reranking and when should you apply it?
- How do you tune similarity thresholds to reduce false positives?
- How can you visualize vector search results in a surveillance dashboard?
- What are the most commonly used vector databases for surveillance?
- How does indexing work in a vector DB (IVF, HNSW, PQ, etc.)?
- How do you choose the right index type for your workload?
- Can you use GPU acceleration with a vector database?
- What are the hardware requirements for large-scale video vector search?
- How do you shard or partition surveillance vector data?
- How often should indexes be rebuilt or updated?
- What strategies help optimize disk usage in video vector storage?
- Can you use cloud-native vector databases for video analytics?
- How do edge devices interact with centralized vector DBs?
- How does search performance scale with millions of video vectors?
- What are best practices for latency-sensitive surveillance environments?
- How do you handle memory constraints in large-scale systems?
- What caching strategies work well with repeated queries?
- How do you monitor and benchmark vector DB performance?
- What are common bottlenecks in surveillance vector pipelines?
- How do you balance accuracy vs. speed in vector search?
- Can vector search run on edge hardware like NVIDIA Jetson?
- What are typical query latencies for large surveillance systems?
- How do you scale vector DB infrastructure across geographies?
- What AI models are commonly used to generate surveillance embeddings?
- How do you fine-tune embeddings for your specific surveillance use case?
- What are the challenges of embedding low-light or noisy video?
- How can face recognition systems integrate with vector search?
- Can action recognition be embedded into vector representations?
- What are trade-offs between general-purpose vs. custom-trained embeddings?
- How do you version and manage changes in embedding models?
- Can models be deployed at the edge to reduce latency?
- How do you handle inconsistent embeddings from different models?
- What are methods to reduce embedding drift over time?
- How do you ensure secure access to surveillance vector data?
- What are best practices for anonymizing sensitive video content?
- How do you implement audit logging for vector queries?
- Can surveillance vector databases comply with GDPR or CCPA?
- What permission models work best for surveillance applications?
- How do you manage user roles in systems with video access?
- How do you protect against malicious queries or re-identification attacks?
- What encryption standards are recommended for vector storage?
- How can you restrict access by camera or location?
- Are there open-source privacy-preserving vector DB solutions?
- How do you perform trend detection using vector databases?