First, give conclusions that can be used for decision-making
The RAG runs from authorized sources of knowledge, suitable for regular updates of systems, products, projects and facts, and allows for reference and permission-based filtering. The fine-tuning changes in model behaviour through samples are more appropriate for fixed output formats, domain expression, classification or tool selection, but does not reliably remember the fact of continuous change, nor does it automatically resolve privileges and references. Many of the effects come from job definitions, data quality or inadequate evaluation.
What conditions need to be identified before judgement is made?
The same question may have different answers under different business, data and project phases. It is suggested that the following conditions be checked and that the common findings on the web be incorporated into their own projects.
Suggested order of advance
First, we'll be clear about the target and the border.
Establish task sets and measure the quality of the underlying model.
Validation Key Dependence
Check how many problems the tips, rules and RGs can solve.
Development of assessable outcomes
A small fine-tuned comparison of remaining stable behavioural gaps.
Make sure you decide the next step with the real results.
Use of independent test sets to check for gains, generalizations and side effects.
How do you understand it in the actual business?
The client service needs to answer the frequently updated product policy, and the RAG should be used to read up-to-date information and display references; if the model is not always able to extract the type of work order according to the firm's fixed JSON field, fine adjustments can be evaluated when there are many correct samples. Ultimately, key fields will still need to be checked in the rules. The examples do not represent the performance of a particular client, and the actual conclusions need to be verified in conjunction with the enterprise's own business volume, sample, system and responsibility boundaries.
The easiest pit to step on.
We'll try to fine-tune the business facts that change more often.
Training sets and test combinations, which make the effects look weak
The fine-tuning of assets after the base model upgrade needs to be reassessed
How should we end up receiving and confirming?
Compare baseline, RAG and fine-tuning programmes with the same stand-alone task set to record target indicators, serious errors, citations, delays and costs.
When preparing to communicate with suppliers or internal teams, it is recommended that current processes, representative samples, existing systems, planning time and budget levels be brought. First, the unknown items are clearly marked, and then the decision is made to use diagnostics, PoC, fixed-range projects or ongoing research and development, which is usually more reliable than a direct demand for a price and duration without borders.