LlamaIndex RAG Malta

We audit the Malta organisation's knowledge landscape — document types, volumes, update frequencies, and query patterns. We define retrieval requirements — what types of questions users will ask, what accuracy is required, what response latency is acceptable — that determine index architecture and query strategy design.

We implement document ingestion pipelines covering all required source types — SharePoint, Google Drive, Confluence, databases, file shares. We select appropriate parsers for each document type, design chunking strategies that preserve semantic coherence, and implement incremental update handling so the index remains current as source documents change.

We design the index architecture — which index types to use, how to structure hierarchical or multi-index configurations, which vector store to deploy, and how to organise metadata filtering. Index design decisions directly affect retrieval quality and query performance for the Malta application's specific content and query characteristics.

We implement query engines with strategies matched to application query types — sub-question decomposition for complex analytical queries, summary retrieval for high-level questions, hybrid search for keyword-sensitive queries. Query engines are tested against representative query sets to validate retrieval quality before integration.

We configure the LLM used for response synthesis — selecting model, designing response synthesis prompts, and implementing output formatting for the Malta application's interface requirements. Citation and source attribution are implemented where users need to verify retrieved information against source documents.

We evaluate the complete RAG pipeline using LlamaIndex evaluation components — measuring faithfulness, relevance, and accuracy against ground truth datasets. Deployment is followed by production monitoring of retrieval and response metrics, with ongoing optimisation as query patterns and document collections evolve.