Engineering Hub

Artificial Intelligence

Production-grade AI requires moving beyond prompt engineering into robust RAG pipelines, agentic reasoning, and custom embedding models.

Core Concepts

Retrieval-Augmented Generation (RAG)Agentic ReasoningVector EmbeddingsSemantic CachingModel Fine-tuning

Technology Stack

QdrantLangChainOpenAIHuggingFaceFastAPI

Architecture Patterns

[User Query] -> [Semantic Cache] -> (Hit) -> [Response]
                         |
                       (Miss)
                         |
                         v
              [Query Transformation]
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                         v
               [Vector DB Search] <--- (Context) ---> [Knowledge Graph]
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                         v
               [Prompt Synthesis]
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                         v
                    [LLM Core]

Implementation Examples

# Semantic Caching Layer
def get_cached_response(query):
    query_vector = embed(query)
    cache_hit = redis_vector.search(query_vector, threshold=0.95)
    if cache_hit:
        return cache_hit.response
    return None