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how many times did i say β€œstructure”? πŸ˜‚ but that’s kind of the whole point of vectorless rag. and honestly, nothing is β€œdead”. traditional rag vs vectorless rag: it’s all just tradeoffs at the end
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Fixed Size Chunking: This is the simplest and most commonly used approach. In fixed-size chunking, the document is split into chunks containing a fixed number of tokens or characters. Each chunk is st
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If you want to visualise how reranking actually works… I can share a simple 5-line CODE using Cohere’s API. And also a full RAG + reranking example, so you can see exactly where it fits in your pipel
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This is episode 9 of learning advanced RAG concepts in the simplest way possible. Each episode is one concept that can genuinely level up your basic chatbot. Today’s episode is on the DATA PIPELINE T
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This is Episode 8 on Advanced RAG concepts. Each episode is single concept that can genuinely level up your basic chatbot. Todays concept is GENERATION in RAG [citations in AI, reduce hallucination
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This is episode 7 of learning advanced RAG concepts in the simplest way possible. Each episode is a single concept can genuinely level up your basic chatbot. And today’s concept is SEMANTIC CACHING
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Distillation attack - 1. what is it, 2. mitigation strategies, 3. what to do as an ai engineer?
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Chinese AI companies (Deepseek, Moonshot AI, MiniMax) distillation attack on U.S AI (Claude by Anthropic).
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Chapter 1 - Understanding the intuition of Embeddings (representation of words in 2D space) Chapter 2 - How connected to RAG & Why it matters (they capture meaning, so bad embeddings kills retrieval)
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Few production RAG considerations: Data pipeline: Your Pdfs are broken = garbage output. Clean before you chunk. Chunking: Too small? You lose context. Too big? You dilute relevance. Break on meani
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This is Ep5 of advanced RAG concepts explained in the simplest way possible. Each episode helps you learn one concept that can genuinely level up your basic chatbot. Today’s episode is on Hybrid Sear
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Metadata is data about your data. These extra details attached to each chunk in your RAG system, provide the context needed for LLM to know what exactly to look for. If you want to implement this, yo
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About @ai.engineeringgg

Akanksha | AI Engineer is a Instagram nano influencer in the Education category. With 3.7K followers and 12 posts, ai.engineeringgg's account shows a follower-to-following ratio of 39.7:1. View all content anonymously on Pictame - no login required.

ai.engineeringgg's engagement rate sits at 14.98%. That's 200% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. Content shows up about every 4 days. Captions lean detailed, averaging 508 characters.

14.98% engagement rate β€” 200% above average for 1K-10K follower accounts.
Posts about every 4 days.
39.7:1 follower-to-following ratio.

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@ai.engineeringgg

Akanksha | AI Engineer

PublicEducation
Posts
12
Followers
3.7K
Top Hashtags
#artwork#anime#beach#baby#beautiful#cat#cute#christmas

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Engagement
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learning, engineering & sharing what works in ai era

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