AI/ML OpsPractitioner

LLM Application Engineering

RAG, agents, evals, observability — production AI

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Two full lessons from LLM Application Engineering — exact topics, hands-on lab pairings, same depth as the paid course. Watch the videos free; sign up to access labs + the rest of the curriculum.

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Lesson 0122 min

When to use LLMs vs traditional ML

Most teams reach for LLMs because they're trendy, when traditional ML would be cheaper, faster, more accurate. We frame the decision tree for picking between rule-based, classical ML, fine-tuned BERT, and frontier LLMs.

What this lesson teaches

· Decision tree: rules → regex → classical ML → small LM → frontier LLM
· When LLMs are the wrong tool — classification, structured extraction at scale
· Cost math: GPT-4 vs Claude vs Llama 70B vs your own fine-tune
· Latency budgets: when 2-second LLM responses are unacceptable
· The 'eval gap' — what makes LLM systems hard to test

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Lesson 0230 min

Building a production RAG system

Hands-on walkthrough of a real Retrieval-Augmented Generation pipeline: ingestion, chunking, embeddings, hybrid retrieval (BM25 + vector + reranking), grounded generation. The same pattern we ship for clients.

What this lesson teaches

· Chunking strategy: fixed-size, semantic, hierarchical — when each
· Embeddings: OpenAI vs Voyage vs Cohere vs open-source — cost + quality
· Vector stores: Pinecone vs Weaviate vs pgvector vs Qdrant
· Hybrid retrieval: BM25 + dense vectors + cross-encoder reranking
· Grounding: forcing LLM to cite source chunks (and verifying)

● Paired lab

Build a RAG over the AWS docs (50K pages) with hybrid retrieval + Anthropic Claude.

These are 2 of 50 lessons. Subscribe to @computerpathshala654 for new lessons + course launches. The full 48 remaining lessons are included with cohort enrolment, with a 7-day money-back guarantee.

What you’ll build