Technical articles, architectural blueprints, and production engineering practices written by AI Consultant Rohit for developers, CTOs, and AI leaders.
Why standard LLM wrappers fail in enterprise settings and how stateful graph orchestration (LangGraph/CrewAI) enables deterministic agent behavior.
How combining Qdrant dense vectors with BM25 sparse keyword retrieval and Cohere rerankers eliminates 99% of RAG hallucinations.
A practical guide to serving open-weight LLMs (Llama 3, DeepSeek) at 10x higher throughput using PagedAttention and CUDA kernel tuning.
Quantitative frameworks for stress-testing JSON tool-calling accuracy, schema validation failures, and automated self-correction loops.
Connecting unstructured document embeddings with Neo4j entity graphs to enable multi-hop reasoning over enterprise knowledge bases.
Implementing voting protocols, adversary check agents, and input sanitization to prevent prompt injection and unauthorized execution.
A decision matrix for CTOs choosing between LoRA fine-tuning, retrieval augmentation, and hybrid architectures based on latency and cost.
Architectural patterns for deploying multi-agent PR auditors that run static analysis, write unit tests, and verify vulnerability fixes.
Deep autoencoder architectures for processing high-throughput financial streaming data in Apache Kafka with sub-150ms latencies.
Generating differential-privacy compliant tabular datasets for medical and financial ML model training without exposing PII/PHI.
Combining XGBoost gradient boosting with PyTorch LSTM neural networks for high-precision e-commerce inventory demand forecasting.
Building zero-retention private RAG pipelines over millions of PubMed papers and electronic health records under strict compliance.
Reducing API token costs by 65% using semantic prompt compression algorithms and KV context caching layer patterns.
Production lessons learned from running long-lived multi-agent crews with state checkpoints, fallback agents, and rate-limit retries.
Comprehensive performance, latency, and HNSW index memory benchmarks comparing Qdrant, Pinecone, Milvus, and pgvector.
Step-by-step methodologies for calculating net financial ROI, developer velocity gains, and API cost reductions for executive sponsors.
Combining episodic vector memory, semantic entity stores, and working memory buffers for continuous multi-session AI agents.
Engineering completely offline, air-gapped LLM deployments for defence, finance, and critical infrastructure with zero internet egress.
Direct answers regarding Agentic AI consulting and implementation.