[Refers to / transferred from the Prompt API ]
As a possible complex local application to prompting, Retrieval-Augmented Generation (RAG) enhances AI systems by dynamically retrieving external (to the model) information to improve response accuracy and relevance. Agentic RAG extends these capabilities with autonomous decision-making and iterative refinement, particularly valuable in local deployments for enhanced privacy and customization.
Some of the main RAG use cases include:
- Enhanced search & question answering: RAG improves search engines with up-to-date featured snippets and powers domain-specific QA systems that combine proprietary data (e.g., medical literature or legal documents) with foundational LLM knowledge.
- Healthcare: Provides clinicians with real-time access to medical guidelines and research.
- Legal: Accelerates case law research and compliance checks.
- E-commerce: Delivers personalized product recommendations using customer behavior data.
- Content generation: generates context-aware summaries, reports, and marketing copy by retrieving relevant source materials.
- Enterprise knowledge management: enables chatbots to answer internal queries about company policies, manufacturing protocols, or regulatory updates.
Agentic RAG introduces autonomous decision-making layers to traditional RAG workflows (by using the LLMs not only for generating answers, but also preparing the prompts), enabling:
- Adaptive problem solving: customer support agents offering discounts proactively.
- Continuous learning: medical diagnosis systems updating with new research.
- Multi-step reasoning: legal tools cross-referencing precedents and statutes.
- Local deployment advantages: privacy-focused healthcare data analysis.
Traditional RAG relies on cloud-based models/vector DBs, where limited customization due to API constraints.
Agentic RAG in a local deployment have advantages like:
- Data privacy: runs fully offline via tools like Langchain/Qdrant.
- Customization: supports domain-specific models (e.g., Gemma-3 for multilingual tasks).
- Cost Efficiency: eliminates API fees using local LLMs.
- Transparency: provides verifiable source citations from internal databases.
A local Agentic RAG pipeline (e.g. RAGapp deployed via Docker) typically includes:
# Simplified architecture
local_llm = Ollama(model="llama3") # Local model
vector_db = Qdrant(embeddings=FastEmbed()) # On-premise storage
agent = LangchainAgent(
tools=[KnowledgeBase(retriever=vector_db)],
system_prompt="Generate markdown responses with citations"
)This approach is particularly impactful in regulated industries like healthcare and finance, where data sovereignty and low-latency responses are critical.
In the CG, please consider/discuss/decide if any of these use cases would make it worth developing support as a new type of standardized assistance API, or as example application of prompting (if apps are a better place to manage RAG).
[Refers to / transferred from the Prompt API ]
As a possible complex local application to prompting, Retrieval-Augmented Generation (RAG) enhances AI systems by dynamically retrieving external (to the model) information to improve response accuracy and relevance. Agentic RAG extends these capabilities with autonomous decision-making and iterative refinement, particularly valuable in local deployments for enhanced privacy and customization.
Some of the main RAG use cases include:
Agentic RAG introduces autonomous decision-making layers to traditional RAG workflows (by using the LLMs not only for generating answers, but also preparing the prompts), enabling:
Traditional RAG relies on cloud-based models/vector DBs, where limited customization due to API constraints.
Agentic RAG in a local deployment have advantages like:
A local Agentic RAG pipeline (e.g. RAGapp deployed via Docker) typically includes:
This approach is particularly impactful in regulated industries like healthcare and finance, where data sovereignty and low-latency responses are critical.
In the CG, please consider/discuss/decide if any of these use cases would make it worth developing support as a new type of standardized assistance API, or as example application of prompting (if apps are a better place to manage RAG).