Data Quality Is the Key to AI Success #
Imagine trying to help a child score 100 points on a test, but giving them a reference book full of typos. How could they possibly learn well? For AI, “Quality In, Quality Out” is no exaggeration. Good data leads to good AI performance; messy data makes AI talk nonsense!
Four Key Aspects of Data Quality #
- Avoid “Garbage In, Garbage Out”: If data is incorrect, outdated, or irrelevant, the AI will learn the wrong things and produce inaccurate or odd answers.
- Accuracy and Relevance: For targeted applications (like customer service bots), the dataset must cover product info, common Q&A, and terminology, with accurate content.
- Generalization Ability: Diverse data helps AI learn to extrapolate, so it won’t get stuck when faced with new questions.
- Enhanced User Experience: Only well-trained AI based on quality data can be truly smart and reliable, ensuring end-user satisfaction and trust.
Step-by-Step Guide to Building High-Quality Datasets #
In Advantech’s lab, we have a streamlined process for building datasets! Here are our practical steps to help you work smarter, not harder:
1. Data Collection and Initial Filtering #
- Prioritize Internal Data: Internal product specs and technical documentation are valuable assets closely aligned with business needs.
- Supplement with Public Data: Utilize high-quality open datasets to enrich your data.
- Define Fine-Tuning Goals Clearly: Clarify the problem you want to solve first, so you can select the right data.
2. Data Cleaning and Preprocessing #
- Denoising: Remove typos, garbled text, and unnecessary symbols.
- Deduplication: Delete duplicate samples to prevent AI from memorizing repeated content.
- Length Control: Adjust data length based on the model’s “memory”; too long or too short is not ideal.
- Tokenization: Convert text into “building blocks” that AI can understand.
- Balance: For classification tasks, ensure balanced data across categories to avoid bias.
3. Data Augmentation #
When data is insufficient or you want to boost diversity, try these techniques:
- Synonym Replacement: Rephrase without changing meaning.
- Back Translation: Translate to English and back, changing sentence structure while keeping the meaning.
- Random Insertion/Deletion/Swap: Increase sentence structure variety.
- LLM-Based Augmentation: Let AI generate more variants directly—super convenient!
4. Dataset Splitting #
- Training Set: Most data is used to teach the AI.
- Validation Set: Used during training to check the AI’s learning and avoid rote memorization.
Dataset Format Revealed: Simple and Clear JSON Q&A Pairs #
We strongly recommend organizing data in JSON format, which is especially suitable for Q&A, summarization, intent recognition, and similar tasks. For example:
[
{
"instruct": "What processor is integrated into the AIR-100 system?",
"output": "The AIR-100 system is integrated with an Intel Atom Processor E3950."
}
]
Three Key Advantages of JSON Format #
- One-to-one input-output, clear and straightforward
- Suitable for various fine-tuning tasks
- Extensible: Easily add fields like source, tags, etc., for greater flexibility
Cloud Models: Exploding Dataset Productivity #
Need large, diverse, high-quality datasets? Manually organizing them is too time-consuming! Now, cloud language models (like ChatGPT, Gemini, Azure OpenAI) are your super assistants for dataset generation.
Superpowers of Cloud Models #
- Efficient Mass Data Generation: Generate thousands of Q&A pairs with a single click, saving time and effort.
- High Quality and Diversity: AI-generated content is natural and professional; with well-designed prompts, you can control style and difficulty.
- Cost Savings: No need for a manual annotation team—save significant manpower and time.
- Great for Data Augmentation: Quickly fill in gaps across scenarios when existing data is insufficient.
- Generate Test Data: Simulate real-world situations to assess the AI’s generalization ability.
Practical Tips #
- Precise Prompts Are Key: Clearly specify what you want, output format, and any important details.
- Iterative Validation and Adjustment: Always review and refine AI-generated content to ensure quality.
Local Data Security Guardian: GenAI Studio Dataset Generator #
Is your data too sensitive for the cloud? No worries—Advantech GenAI Studio’s Dataset Generator offers a local solution:
- Local Processing, Security First: Run entirely on-premises, using Mistral or flexible local models—your data never leaves your organization.
- Multi-format Support: Compatible with
.pdf,.docx,.txt,.xlsx, eliminating tedious file conversion. - Semantic Splitting with Context Preservation: Proprietary algorithms automatically segment documents while retaining context, producing more natural and relevant Q&A pairs or summaries.
All of this enables secure LLM fine-tuning for highly confidential data, maximizing your enterprise AI potential!
Prompt Engineering: The Magic Spell of AI Data Generation #
Designing prompts is like giving the AI a super-clear task brief. The more specific, the more accurate the output!
Essential Elements of a Good Prompt #
- Role Assignment: Have the AI play a professional role (e.g., technical analyst).
- Task Instructions: Clearly specify what to do (e.g., generate Q&A pairs from a manual).
- Output Format: Specify JSON format output.
- Constraints: What content is allowed or forbidden.
- Reference Materials: Provide the source text.
- Examples: Supply input-output samples to guide the AI.
Practical Example #
You are a professional technical documentation analyst specializing in extracting information from provided product manuals and generating clear question-and-answer pairs.
Your task is to read the "Product Manual Excerpt" provided below and generate a minimum of 5 and a maximum of 10 question-and-answer pairs from it.
Each question-and-answer pair should consist of a common user question (instruct) and a direct, precise answer (output). The answers must be based entirely on the information within the provided "Product Manual Excerpt"; do not speculate or add any additional information.
Please output the result in JSON format, where each question-and-answer pair is an object containing "instruct" and "output" keys.
Product Manual Excerpt:
"The AIR-100 system is equipped with an Intel Atom Processor E3950 and comes with 8GB DDR4 memory. Its operating temperature range is -20°C to 60°C, supporting two Gigabit Ethernet ports and four USB 3.0 interfaces. For storage, it provides one M.2 slot for NVMe SSD. The product dimensions are 150mm x 100mm x 30mm."
Output Format Example:
[
{
"instruct": "What processor is integrated into the AIR-100 system?",
"output": "The AIR-100 system is integrated with an Intel Atom Processor E3950."
},
{
"instruct": "How much RAM does the AIR-100 system have?",
"output": "The AIR-100 system has 8GB DDR4 memory."
}
]
Advanced Techniques #
- Chain-of-Thought: Have the AI generate complex content step by step.
- Negative Constraints: Clearly specify what content must not be generated.
- Temperature Parameter Adjustment: Control the creativity of outputs (low temperature = more stable, suitable for factual tasks).
Advantech’s Ongoing Innovation and Vision #
This LLM fine-tuning dataset experiment demonstrates Advantech’s leadership and innovation in AI applications. Whether it’s meticulous dataset preparation, agile use of cloud and local tools, or our proprietary GenAI Studio Dataset Generator, we continually push technological boundaries to deliver smarter, more reliable AI solutions.
Looking ahead, we will keep advancing AI data engineering, optimizing data collection, cleaning, augmentation, and fine-tuning processes, helping enterprises quickly adopt AI and seize new opportunities in the smart market. If you’re interested in Advantech’s AI solutions, feel free to contact us—let’s usher in a new era of data-driven intelligence together!
Want to learn more or need assistance? Advantech will continue to support you on your AI R&D journey—stay tuned for our next technological breakthrough!