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techwithprateek

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Here’s the production-grade way to think about it 👇

The Ingestion Illusion
Embedding 10M PDFs upfront is pure waste
Most documents are never queried
Instead:
→ Fingerprint PDFs → dedupe 30–40% instantly
→ Chunk semantically, not by fixed tokens
→ Embed on access, not on arrival

Payoff: 5 TB shrinks to ~3 TB. Embedding bill drops 60%

The Vector Tax
Vector search is expensive when it’s your first filter
Cosine similarity shouldn’t touch cold data
Instead:
→ Keyword + metadata filter first
→ Narrow to top 1–5% corpus
→ Run vectors only on survivors

Payoff: P95 latency improves 4–6×

The Retrieval Funnel
One retriever is brittle at this scale
Instead:
→ BM25 for recall → vectors for relevance
→ Rerank top 50, not top 5,000
→ Cache query embeddings aggressively

Payoff: Recall stays high. Cost stays flat.

The Context Budget Trap
More context ≠ better answers
It’s noise inflation
Instead:
→ Compress chunks with summaries
→ Enforce hard token caps
→ Track answer attribution coverage

Payoff: Token usage drops 70%. Accuracy goes up.

Reframe: RAG is a retrieval system, not an embedding project.

🔖 Save this for your next RAG system design interview
💬 Comment “RAG” if you are also building a real-world architecture
➕ Follow for production-grade system design, not toy demos 0:07
Here’s the production-grade way to think about it 👇 The Ing...
49,908 687
Feb 15, 2026
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 0:09
Everyone thinks LLM leaks are “model problems.” Actually: th...
9,369 187
Feb 23, 2026
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