Arize Phoenix observability platform setup for LLM debugging and evaluation
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MikeTreml/MissionControl - Page 29
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PII detection and redaction utilities for privacy-compliant conversational AI
Pinecone vector database setup, configuration, and operations for RAG applications
Token-efficient prompt compression techniques for cost optimization
Prompt injection detection and prevention for secure LLM applications
Structured prompt template creation with variables, formatting, and version control
Qdrant vector database with filtering, payloads, and quantization support
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
Batch embedding generation with caching, rate limiting, and multiple provider support
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
Query expansion, HyDE, and multi-query generation for improved retrieval
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
Rasa NLU pipeline configuration and training for intent and entity extraction
Redis backend for conversation state persistence and caching
Microsoft Semantic Kernel planner and plugin setup for orchestrated AI
SetFit few-shot learning for efficient intent classification with minimal data
spaCy NER model training and entity extraction for conversational AI
Weaviate vector database setup with GraphQL queries and hybrid search
Zep memory server integration for long-term conversation memory and user profiling
Provide implementations of advanced data structures
Generate visual representations of algorithm execution
Interface with AtCoder for Japanese competitive programming contests
Profile code performance and identify bottlenecks
Manage and generate competitive programming templates
Interface with Codeforces API for contest data, problem sets, and submissions
Calculate combinatorial values with modular arithmetic
Automated Big-O complexity analysis of code and algorithms. Performs static analysis of loop structures, recursive call trees, space complexity estimation, and amortized analysis with detailed derivation documents.
Track progress through CSES Problem Set with structured learning
Select optimal data structure based on operation requirements
Apply advanced DP optimizations automatically
Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.
Assist in designing optimal DP states and transitions
Model optimization problems as network flow problems
Implement computational geometry algorithms
Provide robust computational geometry primitives
Select optimal graph algorithm based on problem constraints
Convert problem descriptions into graph representations
Curated bank of interview problems organized by company, pattern, and difficulty. Provides problem recommendations, coverage tracking, weak area identification, and premium problem alternatives for FAANG interview preparation.
Simulate realistic coding interview experience
Identify and verify loop invariants for correctness proofs