Understanding RAG and LLMs

Lack of specific information

Hallucinations

Generic responses

Retrieval Augmented Generation

Random Access Generation

Rapid Augmented Generation

Data collection

Data chunking

Document embeddings

To break down large datasets into smaller pieces

To convert text data into embeddings

To handle user queries

To convert source data into vector representation

To retrieve relevant information

To generate responses

Generating responses with an LLM

Handling user queries

Data chunking

Text summarization

Personalized recommendations

Business intelligence

Integration complexity

Data quality

Scalability

Invest in content curation

Use random data sources

Ignore data quality