As an AI Engineer.

@rohit-ghumare · LinkedIn post · rohit-ghumare_as-an-… original · 2026-08-13

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As an AI Engineer. Please learn

Harness engineering, not just prompt engineering

Context engineering, not just long prompts

Prompt caching vs. semantic caching tradeoffs

KV cache management, eviction, reuse, and memory pressure at scale

Prefill vs. decode latency and why they optimize differently

Continuous batching, paged attention, and throughput optimization

Speculative decoding vs. quantization vs. distillation tradeoffs

INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

Structured output failures, schema validation, repair loops, and fallback chains

Function calling reliability, tool contracts, argument validation, and idempotency

Agent guardrails, loop budgets, tool budgets, and termination conditions

Model routing, graceful fallback logic, and degraded-mode UX

RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

Retrieval evals: recall, precision, grounding, attribution, and citation quality

Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals

LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift

Cost attribution per feature, workflow, tenant, and user journey not just per model

Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries

Multi-tenant isolation, cache safety, and cross-user context contamination prevention

Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool

Latency, quality, cost, and reliability tradeoffs across the full inference stack

Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

Shipping LLM systems as reliable infrastructure, not demos wrapped around prompts

100% Opensource.

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