@ConsciousRide: 90% of AI Engineering interviews in 2026 come down to these 7 points. 1. LLM Fundamentals: tokenization, transformers &…

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Summary

A Twitter thread outlines the seven key areas that will dominate AI engineering interviews in 2026, including LLM fundamentals, RAG systems, agentic workflows, inference optimization, evaluation, MLOps, and production realities.

90% of AI Engineering interviews in 2026 come down to these 7 points. 1. LLM Fundamentals: tokenization, transformers & attention, fine-tuning (LoRA/QLoRA), context management, model selection 2. RAG Systems: chunking strategies, embeddings, vector databases, retrieval & reranking, hallucination mitigation 3. Agentic Workflows: tool calling & function calling, ReAct/Plan-Execute patterns, memory & state, multi-agent orchestration 4. Inference Optimization: quantization (AWQ/GGUF), serving engines (vLLM/TGI), batching & KV cache, latency vs cost tradeoffs 5. Evaluation & Observability: LLM-as-judge evals, custom metrics, A/B testing, drift detection, prompt/response logging 6. MLOps Pipelines: experiment tracking, model versioning & registries, CI/CD for AI, data pipelines, deployment automation 7. Production Realities: safety guardrails & prompt injection, scaling inference, cost optimization, debugging failures, compliance & reliability
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Cached at: 06/17/26, 10:04 PM

90% of AI Engineering interviews in 2026 come down to these 7 points.

  1. LLM Fundamentals: tokenization, transformers & attention, fine-tuning (LoRA/QLoRA), context management, model selection

  2. RAG Systems: chunking strategies, embeddings, vector databases, retrieval & reranking, hallucination mitigation

  3. Agentic Workflows: tool calling & function calling, ReAct/Plan-Execute patterns, memory & state, multi-agent orchestration

  4. Inference Optimization: quantization (AWQ/GGUF), serving engines (vLLM/TGI), batching & KV cache, latency vs cost tradeoffs

  5. Evaluation & Observability: LLM-as-judge evals, custom metrics, A/B testing, drift detection, prompt/response logging

  6. MLOps Pipelines: experiment tracking, model versioning & registries, CI/CD for AI, data pipelines, deployment automation

  7. Production Realities: safety guardrails & prompt injection, scaling inference, cost optimization, debugging failures, compliance & reliability

Thanks bro

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