A Practical Guide to Evaluating and Selecting Domestic AI Large Models

1. Background: From Hype to Practical Usability

Over the past two years, domestic large language models have gone through a process from密集 releases to gradual consolidation. In the early days, the focus was on whether such models existed at all. Today, more and more developers and enterprises are asking whether they are actually usable and deployable. This shift means that evaluating large models can no longer remain at the level of asking a few interesting questions and glancing at the answers. Instead, it requires a systematic, reproducible evaluation methodology that stays close to real business scenarios.

This article does not recommend or review any specific product. Rather, it discusses from a general technical perspective how to scientifically evaluate the capability boundaries of a large language model and which key dimensions to focus on during practical selection.

2. Why Casual Testing Is Unreliable

Many developers are used to opening a chat interface, asking a few questions off the top of their heads, and then judging by intuition whether a model works. This approach has several obvious flaws:

1. Sample bias: A handful of questions cannot cover the full scope of a model’s capabilities, making it easy to draw conclusions from incomplete evidence.
2. Lack of reproducibility: Every question is phrased differently, leading to large variations in results and making horizontal comparison impossible.
3. No baseline: Without a reference standard, there is no way to tell whether an answer is good or merely looks good.
4. Ignoring task types: Different tasks—reasoning, generation, extraction, coding—place vastly different demands on model capabilities.

To obtain meaningful conclusions, evaluation must be treated as an engineering discipline.

3. Five Core Dimensions of an Evaluation Framework

3.1 Knowledge Accuracy

This examines how well a model masters factual knowledge. The key is not whether the model knows something, but whether it honestly expresses uncertainty when unsure, rather than fabricating plausible-sounding answers.

Suggested evaluation methods:

• Construct a set of factual questions with clear reference answers.
• Also construct a set of trap questions—those with no definitive answer or with false premises.
• Observe the difference in model performance across the two categories.

3.2 Reasoning and Logical Consistency

This examines model performance on multi-step reasoning, conditional constraints, and causal judgment tasks. It is an important indicator for distinguishing between different capability tiers of models.

Typical test formats include:

• Math word problems requiring multi-step calculation
• Logical reasoning problems such as scheduling and constraint satisfaction
• Information integration and contradiction detection across long texts

3.3 Instruction Following

Whether a model can accurately understand and execute complex instructions directly determines its usability in real business scenarios. For example:

Translate the following text into English, keep technical terms unchanged,
output in JSON format with two fields: original and translated,
and do not add any extra explanation.

Such instructions simultaneously test format constraints, content constraints, and boundary constraints, making them excellent evaluation material.

3.4 Long-Context Handling

As business scenarios grow more complex, the inputs models need to process are becoming longer. Evaluating long-context capability requires more than checking how many tokens are supported. It also requires examining:

• Recall rate of key information within long texts
• Cross-paragraph information integration
• Performance degradation curves as context length increases

3.5 Safety and Stability

This includes the ability to refuse harmful requests, how sensitive topics are handled, and performance stability under adversarial prompts. This dimension is especially critical in enterprise-grade applications.

4. Engineering Recommendations for Evaluation in Practice

4.1 Build a Standardized Test Set

It is advisable to maintain an internal test set that includes:

• Fixed questions and reference answers
• Scoring rules, whether through human evaluation or automated scoring scripts
• Version records to track the effects of model iterations

4.2 Control Variables

When comparing different models, ensure that:

• Prompts are exactly the same
• Sampling parameters such as temperature and top_p are identical
• Evaluation time windows are close to avoid interference from version differences

4.3 Introduce Automated Evaluation

For large-scale evaluation, you can combine rule matching, semantic similarity, and model-as-judge approaches for initial screening, then conduct human review on critical cases.

# Illustration: basic workflow for batch evaluation
def evaluate_model(model_client, test_cases):
    results = []
    for case in test_cases:
        response = model_client.generate(
            prompt=case["prompt"],
            temperature=0.0
        )
        score = score_response(response, case["reference"])
        results.append({
            "id": case["id"],
            "response": response,
            "score": score
        })
    return results

5. Non-Technical Factors in Model Selection

Beyond capability metrics, enterprises should also consider:

1. Deployment mode: Whether private deployment is supported and whether data leaves the region
2. Cost structure: Whether billing is per token or per call, and what the long-term cost looks like
3. Ecosystem compatibility: Whether mainstream API specifications are supported and how high migration costs are
4. Service stability: SLA commitments, rate-limiting policies, and failure recovery capabilities

6. Conclusion

The overall improvement of domestic large models is evident, but promising future does not mean unlimited freedom today. For developers and enterprises, building a scientific evaluation system, clarifying their own business needs, and rationally understanding model capability boundaries are the prerequisites for making large models truly valuable. It is recommended to adhere to a task-centric principle during model selection, letting data speak rather than being swayed by demo effects or promotional claims.