A pragmatic engineering reference for evaluating and deploying AI in corporate environments: Retrieval-Augmented Generation (RAG), autonomous AI agents, LLM pipelines, and risk governance.
Grounding generative models in internal knowledge bases: Semantic chunking, dense vector embeddings, hybrid keyword search, and prompt augmentation.
How reasoning loops (ReAct), multi-tool execution, external API invocation, and error self-correction distinguish autonomous agents from static chat assistants.
Structured discovery framework to identify high-ROI operational use cases, run controlled proofs-of-concept, and maintain human-in-the-loop oversight.
Demystifying modern machine learning and language model terminology with practical engineering context.
Deep neural networks trained on vast text corpora using transformer architectures, capable of generalized understanding, synthesis, reasoning, and token generation.
High-dimensional numerical representations of semantic meaning stored in specialized databases (Pinecone, Qdrant, pgvector) to enable rapid cosine similarity search.
Organizational frameworks that establish access control lists, prompt redaction, hallucination auditing, and compliance barriers to prevent proprietary data leakage.
Fine-tuning permanently updates model weights with specialized domain data; in-context learning uses prompt engineering and RAG to steer responses dynamically.
The ability of LLMs to output structured JSON function arguments when given schema descriptions, enabling systems to execute SQL queries, APIs, and CLI commands.
Techniques including strict system prompt constraints, citation verification, temperature tuning, and RAG ground-truth matching to prevent factually false AI generation.
Alankritam Infotech assists organizations in scoping controlled AI pilots, implementing RAG knowledge assistants, and automating repetitive IT operations.