Architecture of a Scientific AI Agent: RAG, Embeddings, and Trusted Sources

RAG: The Core of Scientific AI Agent Architecture

Retrieval-Augmented Generation (RAG) represents a paradigm shift from traditional language models. Instead of relying solely on parameters learned during initial training (which quickly become obsolete in a field as dynamic as scientific research), RAG outsources knowledge to living databases that are constantly updated with the latest publications.

Charlie's RAG process operates in three distinct phases. Phase 1: Retrieval — When you ask a question, the agent analyzes your intent, transforms the question into optimized search queries, and simultaneously queries PubMed, PMC, GEO, and Espacenet to retrieve the most relevant documents. This step uses semantic embeddings to find not only obvious lexical matches, but also conceptually related publications.

Phase 2: Augmentation — The retrieved documents are preprocessed, filtered by quality, and their key information is extracted: main results, methodologies, conclusions, limitations. This information is then integrated into the language model's generation context, effectively "augmenting" its knowledge with verifiable and current facts. This temporary augmentation is specific to your question and does not persist beyond the current exchange.

Phase 3: Generation — The language model synthesizes the retrieved information into a coherent and structured response tailored to your level of expertise and search context. Crucial difference: generation is constrained by the retrieved sources. If a piece of information is not found in the documents, Charlie will not invent it. Each statement can be traced back to its original source with a precise reference (DOI, PMID, patent number).

"What's impressive about Charlie is its traceability. Unlike ChatGPT, which can generate non-existent references, every statement made by Charlie points to an actual publication that I can verify. This RAG architecture transforms AI from a risk to scientific integrity into a reliable research accelerator." — Dr. Sophie Chen, Data Manager, INSERM

Semantic Embeddings: Understanding Scientific Language in Depth

Embeddings (vector representations) are the technology that enables Charlie to "understand" the meaning of scientific concepts rather than simply comparing strings of characters. Technically, an embedding transforms a text (word, sentence, paragraph, or entire document) into a high-dimensional vector of numbers (typically 768 or 1536 dimensions) where semantically similar texts are mathematically close in this vector space.

For scientific research, this capability is crucial because the same concept can be expressed in dozens of different ways. For example, "CRISPR-Cas9," "CRISPR genome editing," "CRISPR/Cas9 system," "RNA-guided Cas9 nuclease," and "CRISPR-based gene editing" essentially represent the same concept. High-quality embeddings place all these terms in the same region of the vector space, allowing Charlie to recognize them as equivalent even if the exact words differ.

Charlie uses specialized biomedical embedding models trained on millions of publications PubMed. These models capture not only obvious synonyms, but also complex conceptual relationships: protein-gene relationships, drug-target interactions, disease-symptom associations, taxonomic hierarchies, cause-effect relationships, and methodological nuances. This deep understanding enables much more sophisticated searches than simple keyword matches.

The quality of embeddings directly determines the quality of results. A poorly trained embedding could confuse "p53 mutation" and "p53 expression," or miss the connection between "anti-PD-1 immunotherapy" and "checkpoint inhibitor therapy." That's why Charlie invests heavily in state-of-the-art embedding models, constantly retrained on the latest literature to capture the evolution of scientific language and the emergence of new concepts.

Architectural Differences: AI Agent vs. Generic LLM

Understanding what architecturally distinguishes Charlie from a generic ChatGPT or Claude is essential. A generic LLM operates in "closed-book" mode: it responds solely based on its internal parameters learned during initial training. These parameters freeze knowledge at the training cutoff date (typically 6-12 months prior to deployment). Any subsequent publications are invisible to the model, creating a major problem for a field as dynamic as biomedical research.

An AI agent with RAG architecture such as Charlie operates in "open-book" mode: it dynamically accesses external databases at the time of the query, retrieving the most recent publications (added to PubMed a few hours earlier). This constant updating is impossible for a conventional LLM. In addition, RAG largely eliminates the problem of hallucinations: since generation is constrained by the sources actually retrieved, the agent cannot invent facts that do not exist in the literature.

Traceability is another fundamental architectural difference. A generic LLM generates text without being able to cite verifiable sources (or worse, invents references that seem plausible but do not exist). Charlie , thanks to RAG, maintains an explicit link between each piece of information provided and the source document from which it originates. This traceability is not an afterthought feature, but an intrinsic property of the RAG architecture.

Finally, disciplinary specialization is embedded in the architecture. Charlie uses embeddings trained on PubMed, prompts optimized for biomedical language, filters calibrated for scientific methodological quality, and a structured knowledge base (ontologies, taxonomies, biomedical knowledge graphs). This multi-level specialization produces expertise far superior to that of a generalist model "sprinkled" with a few scientific prompts.