Choose by what your system needs to learn and what it needs to know at answer time. Fine-tuning changes model behavior; retrieval-augmented generation (RAG) supplies external evidence when a response is generated; GraphRAG adds relationships between information to retrieval; and retrieval-augmented fine-tuning (RA-FT) trains a model to work with retrieved material. These patterns can be combined, but they solve different problems—and the acronyms DSFT and RAFT have other meanings in current research.
What do DSFT, RAG, RA-FT, and GraphRAG mean?
In this article, DSFT means domain-specific fine-tuning: adapting a model with examples from a particular field or task. RAG means retrieval-augmented generation: finding relevant external information and placing it in the model’s context before it answers. RA-FT means retrieval-augmented fine-tuning: training a model to use retrieved passages. GraphRAG means RAG that uses graph structure—relationships among entities, claims, or documents—to support retrieval and reasoning.
Those expansions are context-specific, not universal. A 2025 paper by Chen and Chen uses DSFT for “Diffusion SFT,” a masking-and-loss strategy for diffusion language models; a 2026 AAAI paper uses the acronym for domain-specific supervised fine-tuning. A 2024 practitioner article uses RAFT for retrieval-augmented fine-tuning, while a Microsoft-authored paper posted September 17, 2026 uses it for a separate troubleshooting framework. When the acronym appears without explanation, check the paper or product’s own definition.
How do fine-tuning and RAG differ?
Fine-tuning and RAG act at different points in a system. Fine-tuning changes model weights using training examples. RAG retrieves information from an external source at inference time and puts selected material into the prompt or context used to generate an answer. Fine-tuning can shape how a model responds; retrieval can supply evidence about facts that are not encoded in its weights.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →#1 Best Overall
| Pattern | What changes or is added | Useful when | Main operational consideration |
|---|---|---|---|
| Domain-specific fine-tuning (DSFT, as used here) | Model weights are adapted with domain-relevant training examples. | Desired behavior, terminology, task conventions, or response formats are relatively stable. | Requires curated training data and a training pipeline. Fine-tuning alone does not provide a live connection to changing documents. |
| RAG | Relevant external passages are retrieved and supplied as generation-time context. | Answers need to draw on external or changing information. | Answer quality depends on retrieval relevance, context selection, and whether the model grounds its answer in the material. |
| RA-FT (retrieval-augmented fine-tuning) | Training examples teach a model to use retrieved passages, including examples with irrelevant distractor documents. | A team wants to adapt model behavior specifically for tasks involving retrieved context. | It combines retrieval with a fine-tuning process; the label is used by particular authors and is not a universally settled architecture name. |
| GraphRAG | Retrieval uses graph structure to connect entities, claims, or documents; some approaches also use community summaries. | Questions depend on relationships, multiple evidence hops, or themes across a large corpus. | Graph construction adds extraction, indexing, and maintenance work; its value depends on the query mix and corpus. |
These patterns are not mutually exclusive. A system can retrieve current evidence and also use a fine-tuned model, or add graph-based retrieval for queries that need relational context. The extra layers should answer a demonstrated need rather than serve as architecture by default.
When is domain-specific fine-tuning useful?
Consider fine-tuning when the central problem is how the model performs a task—not merely that it lacks access to a fact. Examples include consistently following a specialized response format, applying domain conventions, or learning a stable task pattern from examples. A useful training set needs to represent the behavior the system should reproduce, and operating the approach means managing data preparation and model training.
Fine-tuning is not a substitute for updating a knowledge source. If a policy, product catalog, or technical document changes, changing model weights is not an automatic route to making the revised document available at inference time. A retrieval layer can fetch updated material instead, though the source and retrieval index still need to be maintained.
The acronym DSFT alone does not identify one method. Chen and Chen’s 2025 paper, for example, reports DSFT as “Diffusion SFT” and reports improvements of 5–10% on evaluated mathematical problems and approximately 2% on evaluated logical problems for diffusion-language models. Those are results for that paper’s models and tasks, not a general estimate of the benefits of domain-specific fine-tuning.
Free tools Windows power users keep installed
One-click scans. No signup required.
When is RAG enough, and when might GraphRAG help?
Use conventional RAG for passage-level evidence
When a question can be answered from one or a few relevant passages, conventional RAG is a natural starting point: retrieve candidate text, select context, and generate an answer from it. This provides a route to use external information without retraining the model every time a source document changes. It does not guarantee correctness: irrelevant retrieval, omitted context, or unsupported generation can still produce a poor answer.
Consider GraphRAG for connected or corpus-wide questions
GraphRAG is intended to make relationships among information available to retrieval. In Microsoft’s documented pipeline, documents are chunked; entities and claims may be extracted; communities are identified; and reports and embeddings are produced. A system can then use this structure, alongside text, to retrieve connected evidence. The original Microsoft GraphRAG paper describes entity graphs and community summaries as a way to address global questions such as “What are the main themes in the dataset?”—a question that may not be answered by retrieving a single matching passage.
Rank #3
That is a rationale for evaluating graph-based retrieval, not a universal performance ranking. The paper’s results concern global sensemaking tasks over datasets around the million-token scale; they do not show that GraphRAG outperforms standard RAG for every corpus or query. For a collection dominated by straightforward factual lookups, the graph-building effort may not address the main bottleneck.
Does GraphRAG require a knowledge graph or a graph database?
GraphRAG uses graph structure as part of retrieval, but that does not establish a requirement to adopt one particular graph database product. Implementations can combine graph queries with other retrieval methods: for example, a Google Cloud reference design combines vector search and graph queries. That design is an example of one implementation on Google Cloud, not a platform-neutral requirement.
Recommended Free Tools
Graph structure also has to come from somewhere. Depending on the approach, a pipeline may extract entities and claims from documents, connect them, identify communities, and produce summaries or embeddings. Those steps create additional data-quality and indexing concerns: extraction errors or missing relationships can affect what the system retrieves. The benefit is most plausible when the questions depend on those relationships.
What does RAFT mean in GenAI?
It depends on the source. In the 2024 practitioner usage, RA-FT means retrieval-augmented fine-tuning: training a model on examples that include retrieved passages, sometimes including irrelevant “distractor” documents so the model learns to use context appropriately. Treat this as the terminology of that article, rather than a universally standardized name.
In a separate Microsoft-authored paper posted September 17, 2026, RAFT means Retrieval-Augmented Framework for Troubleshooting Agents. It models closed support cases as timelines and retrieves relevant investigation stages together with their parent case trajectory. The paper reports retrieval-layer evaluations on a synthetic benchmark and Apache Jira issues; it does not establish that every complete production support agent will improve. These are different approaches despite sharing an acronym.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose an architecture?
Start from the system’s data and questions, then add complexity only where an evaluation shows a gap. Use these checks to narrow the design:
- How often does source knowledge change? Frequently changing facts point toward external retrieval. Fine-tuning alone will not make new source documents available at inference time.
- What must the model learn? Stable behavior, domain conventions, or specialized output formats may justify fine-tuning. Retrieval supplies evidence, but does not by itself teach every desired task behavior.
- What shape do the questions take? Passage-level factual questions may suit conventional RAG. Questions about relationships, multiple hops, or broad themes across a corpus are stronger candidates for evaluating GraphRAG.
- Can the team operate the additional pipeline? Fine-tuning requires data preparation and training operations. GraphRAG can add entity or claim extraction, community construction, and indexing. Microsoft warns that GraphRAG indexing can be expensive.
- What evidence must an answer provide? If users need traceable support, evaluate whether retrieved evidence actually supports generated answers and whether the system attributes that evidence clearly.
A practical progression is to establish a baseline with the least complex design that can answer the target questions. If the model lacks stable task behavior, test fine-tuning; if it lacks current evidence, test retrieval; if retrieved passages miss relationships or corpus-wide themes, test graph structure. Compare each version on the same representative tasks, including failures and operational burden.
What should an architecture evaluation measure?
Evaluate on the intended corpus and question mix, not only on a broad benchmark or an attractive demonstration. Track retrieval relevance, grounded answer correctness, evidence attribution, coverage of relational and global questions, latency, and the effort and cost of updating sources or indexes. Include questions with stale, conflicting, or missing source material so the system’s failure behavior is visible.
Keep evaluation claims scoped to the work that produced them. For instance, the 2026 RAFT troubleshooting paper evaluates a retrieval layer, including a synthetic benchmark and real Apache Jira issues with human-created duplicate labels; its abstract describes the Jira evidence as directional. That is not an end-to-end production-agent effectiveness result. Similarly, GraphRAG’s global-question findings should be judged against the kinds of tasks and datasets studied rather than generalized into a claim that it is always better.
What should teams know before adopting Microsoft GraphRAG?
Microsoft’s GraphRAG repository describes the project as largely in maintenance mode, says it will not accept new pull requests or implement new features, and states that the code is a demonstration rather than an officially supported Microsoft offering. The maintainers also warn that indexing can be expensive and recommend starting small; their documentation points to prompt tuning as an important part of using the approach. Treat the repository as an implementation reference, not a guarantee of an actively supported production service. Check the current repository and documentation before making a deployment decision.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
