AI Engineering
Production AI systems, not demos
Focused on grounded retrieval, reliable pipelines, and operational guardrails for educational and enterprise contexts.
RAG pipeline (live sketch)
Retrieval-first flow used for grounded tutoring and assessment context.
{ context: top_k(chunks), policy: "grounded", memory: "redis:ttl" }
streaming tokens…
Retrieval Augmented Generation
Ground model outputs in approved institutional knowledge instead of relying on parametric memory alone.
- • Retrieve top-k relevant chunks before generation.
- • Pack citations/metadata into the prompt context window.
- • Fail closed with a safe response when retrieval is weak.
Ollama
Local/self-hosted model runtime for chat, generation, and embedding workloads.
- • Used for LLM inference and embedding generation.
- • Keeps AI workloads deployable alongside application services.
- • Supports iteration without hard dependency on a single cloud vendor.
Embeddings
Convert material chunks and queries into vectors for semantic similarity search.
- • Chunk by document structure and token budget.
- • Store vectors with rich metadata for filtering.
- • Re-embed on material updates with idempotent upserts.
Qdrant
Vector database powering ANN search for tutoring, search, and generation context.
- • Collections organized by domain/material scope.
- • Payload filters for course, role, or document constraints.
- • Supports re-indexing strategies for evolving corpora.
Prompt Engineering
Instruction hierarchy that prioritizes policy, retrieved evidence, and task format.
- • Separate system policy from user question and retrieved context.
- • Require structured outputs for question generation.
- • Include explicit non-answer behavior for missing evidence.
Question Generation & Validation
Generate assessment candidates, then validate before human planner review.
- • Concept coverage from retrieved materials.
- • Structural validation for question types.
- • Duplicate detection against existing banks.
Streaming & Context Memory
Token streaming for responsiveness; Redis for short-term conversational state.
- • Stream responses to reduce perceived latency.
- • Persist recent turns with TTLs.
- • Keep memory subordinate to retrieved knowledge.
Evaluation & Quality
Lightweight evaluation loops for retrieval relevance and answer groundedness.
- • Sampled human review for high-stakes outputs.
- • Track retrieval hit quality and fallback frequency.
- • Use findings to tune chunking, filters, and prompts.
Architecture
AI-critical diagrams
High-level flows for pipelines that hiring panels and architects expect you to explain clearly.
AI Pipeline
End-to-end path from user query to grounded streamed response.
Nodes
- 1Receive authenticated query
- 2Embed query and retrieve top-k chunks
- 3Assemble prompt with policies + context + memory
- 4Stream tokens to client
Material Processing
Upload to searchable knowledge via extraction and embeddings.
Nodes
- 1Validate and store raw material
- 2Enqueue processing job
- 3Extract normalized text + metadata
- 4Chunk, embed, and upsert vectors
Question Generation Pipeline
From materials to validated assessment candidates.
Nodes
- 1Retrieve concept context from materials
- 2Generate candidate questions
- 3Validate structure and detect duplicates
- 4Faculty reviews in planner before publish