SF exhibits a high degree of meritocracy when the individual is:
-a former employee of Anthropic
- a former employee of OpenAI
- a dropout from Stanford University
- a dropout from the Massachusetts Institute of Technology
- a dropout from Harvard University
- an individual who obtained a PhD at the age of 15
- a former poker champion
- a former chess master
- a Thiel Fellow
- or the child of parents who were employed by Roblox.
CANCEL your weekend plans.
You NEED to:
• Build a RAG system that cites sources with page numbers
• Implement hybrid search (dense + sparse) for better retrieval
• Add reranking with cross-encoders for top-10 accuracy
• Set up chunking strategies (500 tokens, 50 overlap minimum)
• Build query expansion for better recall
• Add metadata filtering for scoped retrieval
• Implement citation grounding to prevent hallucinations
• Create an eval harness with 50+ golden test cases
• Track retrieval metrics: hit rate, MRR, NDCG
• Add fallback to web search when confidence is low
• Build query rewriting for ambiguous questions
• Implement parent document retrieval for context
• Add embedding caching to reduce latency 80%
• Use colbert or late interaction for better accuracy
• Build a RAG dashboard showing retrieval quality
• Test with adversarial queries that should return nothing
• Document your chunking strategy and why it works
• Benchmark against naive RAG and show improvement
You have way too much to do.
Bookmark & Repost.
Huge congrats to @SkyrootA & team for successfully taking Vikram-1 Test Flight-1 to orbit.
India's first privately developed orbital rocket has completed its final burn and injected its payloads into a ~450 km orbit, making India the third country in the world with private orbital launch capability.
🇮🇳🇮🇳