Engineering
notes.
Practical notes on agent search, RAG evaluation, web retrieval, and the API design choices behind SiftQ.
The Shift From Keyword Search to Semantic Web Retrieval
Learn how AI agents and RAG systems are shifting from keyword matching to intent-driven semantic web retrieval. Discover how structured APIs power this change.
Dense Retrieval vs Sparse Retrieval in AI: Key Differences
Compare dense and sparse retrieval in AI. Discover how keyword matches and neural embeddings shape modern search, hybrid architectures, and RAG pipelines
How to Build a Claude MCP Server with SiftQ Web Search API
Learn how to build a custom Claude MCP (Model Context Protocol) server from scratch using Python and TypeScript to power Claude with SiftQ’s ultra-fast, structured Web Search API.
Why Search as a Service Is Critical for the AI Ecosystem
Understand how Search as a Service powers RAG pipelines, resolves LLM knowledge limits, and handles the infrastructure cost of AI agent retrieval.
How Web Search Works: From Crawling to AI Agent Retrieval
Understand how web search works. Discover how crawling, indexing, and ranking deliver structured web context for search engines and real-time AI agents.
Fact-Checking AI Agents: Best Practices & Tools
Learn how to build fact-checking AI agents using advanced retrieval APIs, structured outputs, and the best tools to eliminate LLM hallucinations.
Multi-Agent Systems: Giving Internet Access to Agents
Learn how to empower multi-agent systems by giving internet access to specialized agents. Discover strategies for high-speed web grounding.
How to Evaluate Web Search APIs for AI Applications
Learn how to evaluate web search APIs for AI applications. Discover key benchmarks, metrics, and latency checks to build reliable RAG pipelines.
How to Design a Scalable Web Browsing Tool for ChatGPT Clones
Learn how to design and build a high-performance, low-latency web browsing tool for custom ChatGPT clones using scalable RAG and retrieval APIs.
Building Autonomous Research Agents: Architectural Overview
Explore the core system architecture of autonomous research agents, from orchestration to semantic retrieval pipelines and web grounding strategies.
Sub-200ms Latency: Building Real-Time AI Agents with Instant Web Search
Learn how to build highly responsive, real-time AI agents. Explore structural bottlenecks in RAG pipelines, latency-budgeting, and optimizing retrieval down to sub-200ms.
The Future of Search: Agentic Search vs Traditional Search
Discover the paradigm shift from traditional search engines to Agentic Search. Learn why AI agents need fast, structured, and neural-ranked retrieval to succeed, and how the right Web Search API prevents LLM hallucinations.
Beyond 1 Million Tokens: Optimizing Prompt Context Window Size for RAG Applications
Long context windows are not a substitute for good retrieval. Discover how to build low-latency RAG architectures using dense semantic highlights and cross-encoder ranking.
Bypassing CAPTCHA and Cloudflare for AI Web Scraping (2026)
Scraping the web for AI agents is getting harder. Learn why bypassing CAPTCHA and Cloudflare is a massive engineering trap, and how to use managed APIs instead.
How to Stop LLM Hallucinations Using Live Web Search (2026)
Stop your AI agents from making up facts. Learn why LLMs hallucinate and how to fix the problem by grounding prompts with real-time, low-latency web search APIs.
The Engineering Guide: How to Reduce LLM Token Cost in Web Search
Stop burning your API budget on raw HTML. Learn the exact architecture to extract structured data, drop token consumption by 90%, and build scalable search agents.
Retrieval evaluation for RAG agents
A practical scorecard for measuring whether your retrieval layer is actually helping the model answer with fresher, cited evidence.
Designing search APIs for AI agents
Agents call search differently from humans. They need predictable schemas, explicit scopes, and small enough responses to fit inside iterative reasoning loops.
FRAMES benchmark field notes
Early notes from running retrieval workloads that combine freshness, reasoning depth, and source-level citation pressure.