techwithprateek

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Everyone thinks LLM leaks are “model problems.” Actually: they’re architecture problems. Here’s the framework I use in production Access, Context, Output. ⚡ The Over-Entitled Retriever Insight: Most leaks happen before generation because your retriever sees too much • Enforce row-level ACLs → filter before embedding search • Partition vector indexes by tenant → zero cross-org bleed • Sign queries with user identity → audit every retrieval Result: 100% tenant isolation. Zero accidental cross-access — ⚡ The Prompt Injection Trap Insight: A single malicious sentence can override your system prompt. “Ignore previous instructions…” → goodbye guardrails. • Strip tool instructions from retrieved text → no tool hijacking • Freeze system prompts server-side → never client-controlled • Run injection classifier → block risky queries pre-generation Payoff: 80% of jailbreak attempts stopped before inference. — ⚡ The Memory Time Bomb Insight: Long-term memory becomes long-term liability. • Encrypt embeddings at rest → reduce blast radius • Set TTL on conversation memory → auto-expire after 24h • Disable training retention → no vendor data reuse Outcome: Sensitive data lifespan drops from months to hours. — ⚡ The Output Spill Insight: The model can echo secrets it shouldn’t. Especially in summarization and Q&A. • Add regex + NER redaction → mask PII before response • Apply policy LLM pass → secondary compliance filter • Log every response with hash → traceability under 200ms Result: 90% fewer policy violations. — Secure LLM ≠ better prompts. It’s layered defense. Access → Context → Output. 🔖 Save this before your next security review 💬 Comment “SECURE” if you also building safe LLM Appa ➕ Follow for production-grade AI system design breakdowns
Everyone thinks LLM leaks are “model problems.” Actually: th...
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Feb 25, 2026
Everyone thinks RAG fails because models hallucinate. Actually: your chunks are dumb. If retrieval feeds garbage structure, generation can’t recover. Three upgrades: Semantic Chunking > Token Slicing 500-token splits ignore meaning boundaries. → Split by headings, sections, logical claims → Keep chunks 300–800 tokens max → Add 10–20% overlap for context continuity Payoff: Retrieval relevance improves 30–50%. Aha: Chunk size should match how humans think. Not tokenizer limits. ___ Connection-Aware Retrieval Most teams store chunks like isolated PDFs. But your data has relationships. Policies reference sections. APIs reference schemas. Research cites experiments. → Store metadata: author, version, section, entity → Use hybrid search: BM25 + embeddings → Re-rank top 20 → send top 5 Payoff: Answer accuracy jumps 2×. Latency barely changes. Aha: Retrieval isn’t about similarity. It’s about structure ___ The Knowledge Graph Layer Flat vector stores miss cross-document reasoning. Graphs preserve relationships. Instead of “find similar text” You ask: “What links A → B → C?” → Extract entities + relations during ingestion → Store triples alongside embeddings → Traverse graph, then retrieve supporting chunks Payoff: Multi-hop questions improve 3×. Think of it like this: Vectors = fuzzy memory. Graphs = connected memory. Best systems use both. Chunk smart. Store relationships. Retrieve with structure. 🔖 Save this for your next RAG architecture review 💬 Comment your struggles while building a RAG application ➕ Follow for more production-grade AI system design 0:08
Everyone thinks RAG fails because models hallucinate. Actual...
1,332 59
Feb 26, 2026