In recent years, large language models (LLMs) have become the foundation of many product features—from chatbots to automatic content generation. However, the model alone is often insufficient: there is growing demand for up‑to‑date, trustworthy information as well as for controlling costs and response time. Therefore, in practice hybrid architectures that combine LLMs, Retrieval‑Augmented Generation (RAG), and decision agents are increasingly used. In this article we describe how to design and deploy such an architecture in a real product, with particular focus on the cost‑latency‑privacy trade‑offs.
Why a hybrid architecture?
An LLM can generate fluent and coherent text, but it lacks built‑in access to the latest data. RAG solves this problem by offloading part of the query to an external index and injecting the returned passages as context. Agents, on the other hand, enable sequential task execution—e.g., fetching data, verifying it, and presenting the final result—while maintaining flow control.
Combining the three elements provides:
- up‑to‑date information (RAG),
- interaction flexibility (agents), and
- high language quality (LLM).
Core components of a hybrid architecture
1. LLM – can be large (e.g., a GPT‑like family) or smaller, running in the cloud or on‑device. 2. RAG layer – document index (vector‑based, e.g., FAISS, Milvus) and a search service. 3. Agent – orchestration engine (e.g., LangChain, LlamaIndex) defining steps, conditions, and fallbacks.
At Coderia.it we use open‑source libraries that allow swapping individual components without rebuilding the entire system. This lets us tailor the solution to cost constraints and data‑privacy requirements.
Designing LLM integration with Retrieval‑Augmented Generation
The key step is preparing the index. Documents are split into chunks (typically 200‑500 tokens) and vectorized using an embedding model (e.g., OpenAI Ada embeddings, Sentence‑Transformers). Then:
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)
When a query arrives, the agent first calls vectorstore.similarity_search(query, k=5), retrieving the most relevant passages. These passages are passed to the LLM as part of the prompt, enabling the model to generate a response grounded in real data.
Agent patterns in product systems
In practice, two patterns are most common:
- Single‑phase agent – fetches context, generates a response, and returns the result. Ideal for simple FAQ‑type queries.
- Multi‑phase agent – iterates: first retrieves documents, then assesses their usefulness, optionally calls additional micro‑services (e.g., data validation), and finally generates the final text. This scheme reduces hallucinations and allows better cost control, because the LLM is invoked only in the last phase.
At Coderia.it we implemented a multi‑phase agent in a technical documentation recommendation system. As a result, average token cost dropped by about 30 %, while response time stayed below 800 ms.
Cost and latency of hybrid LLM models
The hybrid does not eliminate costs, but it distributes them. Vector search is usually very cheap (a few hundred tokens per query). The most expensive component—text generation—is invoked only when necessary. Latency depends on three factors: index access time, LLM call time, and any additional micro‑services. Optimization involves:
- locating the index close to the application (e.g., in the same cloud region),
- caching frequently used passages,
- choosing an LLM of appropriate size—smaller on‑device model for simple tasks, larger cloud model for complex generations.
Scaling RAG in the cloud and on‑device
In the cloud, the vector engine can be easily scaled by partitioning the index and autoscaling instances. For mobile apps or high‑privacy requirements, a lightweight FAISS or SQLite‑based VSS can run on‑device. In that scenario:
// example Swift (iOS) code – creating a local index
import Faiss
let index = IndexFlatL2(d: 768)
index.add(vectors: embeddingArray)
Combining both approaches (cloud/on‑device hybrid) enables fast offline responses and background synchronization of the latest documents.
“In a hybrid architecture the most important thing is not how large the model is, but how intelligently you use it.”
Fine‑tuning vs prompting in hybrid solutions
Fine‑tuning involves further training a model on specific data. It provides the greatest control, but comes with computational costs and requires large datasets. Prompting, on the other hand, leverages an existing model and supplies it with real‑time context. In a hybrid architecture we usually choose prompting, because RAG already provides specific context, and additional fine‑tuning does not deliver proportional benefits. Fine‑tuning remains an option for critical cases where highly specific terminology or behavior is required.
Practical implementation checklist
- Define the scope of data to be available in RAG (documentation, knowledge bases, logs).
- Select an embedding model and build a vector index (FAISS, Milvus, Pinecone).
- Define agent scenarios – single‑phase vs multi‑phase.
- Configure a cost limit for LLM calls (e.g., maximum number of tokens per query).
- Implement latency and cost monitoring (Prometheus + Grafana).
- Test privacy scenarios – ensure RAG data stays within regulatory boundaries (GDPR, HIPAA).
- Provide a fallback to static responses in case of LLM failure.
Common mistakes and trade‑offs
1. Over‑reliance on the LLM – invoking the model for every request leads to high costs and increased hallucination. Solution: first assess the relevance of RAG results and call the LLM only when the context is insufficient.
2. Too large a context fragment – exceeds the LLM token limit, forcing the trimming of important information. Solution: rank fragments and dynamically truncate to the most essential sentences.
3. Insufficient privacy safeguards – sending sensitive data to an external LLM. Solution: encrypt queries, use on‑premise or open‑source models that can be hosted within a private network.
4. Lack of quality monitoring – hallucinations may appear only after some time. Solution: automated regression tests with a set of control questions.
5. Suboptimal model choice – using the most expensive model for simple tasks. Solution: layered strategy – a small on‑device model for short answers, a large cloud model for complex generations.
By applying these practices, product teams can build solutions that are fast, cost‑effective, and compliant with legal requirements.
In summary, a hybrid LLM RAG agent architecture combines the latest language capabilities with cost and privacy control. If you want your product to benefit from this technology, feel free to collaborate with Coderia.it – we’ll help you select the right models, build a scalable RAG, and develop agents that meet your business needs.
