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Hot take: Bad chunking breaks more RAG apps than bad prompts. Most people blame embeddings, vector DBs, or the LLM itself… But if your chunks are wrong, retrieval is broken before generation even s
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Stop overcomplicating RAG. Most tutorials lock you into a framework or language β€” but the truth is simpler: RAG is just two pipelines: πŸ“₯ Ingestion (prepare your data) πŸ” Query (retrieve + generate
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AI apps aren’t just β€œsmart” β€” they’re a new security risk layer most teams aren’t prepared for. The biggest vulnerabilities aren’t inside the model… They come from how you integrate it into your syst
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Most AI apps don’t fail because of bad models β€” they fail because of bad engineering decisions. After seeing multiple AI projects break in production, I’ve noticed the same patterns again and again:
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Multi-agent AI isn’t just β€œmore agents” β€” it’s a completely different way of building systems. One agent tries to do everything. Multi-agent systems divide, specialize, and coordinate. That’s where
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Most people think AI β€œremembers” things. It doesn’t. LLMs are stateless by design β€” every call starts fresh. That’s why your AI: β€’ forgets user preferences β€’ repeats questions β€’ breaks across sessi
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AI agents are not hype anymore β€” they’re already here. They can browse the web, write code, run tests, and even deploy software… autonomously. But most developers either: β€’ Overcomplicate them β€’ Or
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Every RAG tutorial picks a language and framework. What if you're not using either? πŸ€” Here's the language-agnostic RAG blueprint β€” works in Python, Go, TypeScript, anything. RAG has exactly 2 phases:
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Most AI backends are held together with duct tape and optimism. 🩹 Here's the architecture blueprint senior AI engineers use β€” and rarely document. A solid AI backend isn't magic. It's 4 clean layers:
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Choosing the wrong vector database today = painful migration tomorrow. Most developers pick based on hype… not use case. Here’s the reality: Pinecone β†’ easiest, but $$$ at scale Chroma β†’ perfect fo
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Most developers don't realize they can run AI completely offline β€” no API keys, no bills, no data leaving your machine. Ollama makes it dead simple. One command to install. One command to pull a model
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Most devs call the AI API directly from their business logic. That's the first mistake. Production AI features need retries, timeouts, rate limit handling, cost tracking, and output validation β€” not j
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About @aidevstack

Mitesh Vanaliya β€’ AI Developer πŸ–₯️ is a Instagram growing creator. With 9 followers and 12 posts, aidevstack's account shows a follower-to-following ratio of 0.2:1. View all content anonymously on Pictame - no login required.

aidevstack's engagement rate sits at 30.56%. That's 511% 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 849 characters.

30.56% engagement rate β€” 511% above average for 1K-10K follower accounts.
Posts about every 4 days.

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aidevstack
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@aidevstack

Mitesh Vanaliya β€’ AI Developer πŸ–₯️

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#LLM#MachineLearning#SoftwareEngineering#AIEngineering#RAG#LearnAI#BuildInPublic#AIForDevelopers

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Building with RAG β€’ Agents β€’ Claude Code I teach devs to build AI apps faster πŸš€ 🎁 Free Claude prompts πŸ‘‡

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