A guide for data professionals to LLM/GPT Prompting for everyday tasks….

As an AI and data professional, I understand the importance of having efficient workflows and accurate data analysis. That’s why I’m excited to share with you a guide on how you can leverage the power of using LLMs in Microsoft OpenAI with Python to streamline your processes and enhance your data analysis capabilities.

Prompt design and structure play a critical role in maximizing the potential of Microsoft OpenAI GPT models. Whether you are using the Chat Completion API or the Completion API, understanding the nuances of prompt formatting is key to obtaining accurate and relevant responses. When designing prompts for the Chat Completion API, it is important to frame the conversation context clearly and concisely.

“Prompt design and structure significantly impact the quality and relevance of the generated text. By investing time and effort into crafting effective prompts, you can unlock the full potential of Microsoft OpenAI GPT models in your data analysis workflows.”

Tips for good prompt design:

  1. Be clear and concise – State the desired outcome or provide specific instructions.
  2. Use sample outputs – Including examples of the desired responses can guide the model’s understanding.
  3. Experiment with temperature – Adjusting the temperature parameter can influence the level of randomness in the generated responses.
  4. Iterate and refine – Prompt design is an iterative process, so don’t hesitate to experiment and fine-tune your prompts.”

There are two kinds of prompts we generally use:

  • General problem, general solution
  • Specific problem, specific solution

The first generally starts with the likes of:

“How do I…” … “What is the…” …”When does…”

Prompt Examples for data engineering

Here are some examples of this in the data sphere:

PROMPT: How do I select only three fields from an SQL database table when the select statement sends back all by default?

To select only three specific fields (or columns) from a table in an SQL database, you need to specify those fields in your SELECT statement instead of using the wildcard *, which selects all fields. Here's the general syntax for selecting specific fields:

In this statement, replace field1, field2, and field3 with the actual names of the columns you want to retrieve, and your_table_name with the name of the table you’re querying.

For example, if you have a table named employees and you want to select the name, email, and department fields, your query would look like this:

This query will return only the values from the name, email, and department columns for all records in the employees table.

For a more specific prompt question, you need to supply a lot more detail – the LLM cannot read your mind! … you need to give it context, and remember, in natural language processing context is king!

Here is a pretty common structure that connects three tables together:

Table-A is customers, each customer can have many departments, and each department can have many users. They are connected to one another using a foreign-key. The question we want to know as a data engineer (albeit junior!) is how can we connect these in an SQL statement and get a list of usernames for a given customer? The main thing to know here is its not a simple ‘general’ problem, rather, its specific, and as such we need to GUIDE the LLM and give it CLUES by way of CONTEXT … so here’s how to do it:

Your ROLE is expert database engineer. I need help in writing SQL code. I have three tables (named A,B,C), each connected to the other using a logic of "A is connected to B, and B is connected to C". Give me an example SQL statement demonstrating how to select a field named 'UserName' from table C, where the ID from table A = "123"

1 – we tell the LLM the ROLE it should play – this helps set the perspective for it
2 – we state the GENERAL CONTEXT AREA by saying ‘we need help in writing SQL code
3 – next, we give it the BACKGROUND CONTEXT which DESCRIBES the detail of the structure we have (tables A..C), and importantly, telling it the LOGIC of how the tables are connected “A is connected to B, and B is connected to C”
4 – finally we we ask the question ‘Give me an example SQL statement…’

Providing all of the best CLUES to the LLM by way of context, allows it to give you the best answer it can predict, and hopefully, the one you are looking for:


Then it EXPLAINS what it did:

Note what’s going on here – the LLM is ‘REASONING OUT’ what it is doing – giving you INSIGHT into its thinking – this is important, as if you don’t get the answer you want, you can tell it WHERE to adjust its reasoning to improve the answer.

Remember, the key to a successful prompt is GIVING CONTEXT and putting guide-rails around what you want the LLM to do.

Data quality, security, and building trust in AI

As AI adoption continues to gain momentum, organizations are placing increasing importance on data quality, security, privacy, and trustworthiness. The effective utilization of high-quality data is crucial for AI projects to deliver accurate and reliable results. Simultaneously, organizations are investing in proactive security measures to mitigate the risks associated with AI implementation, such as data breaches and cyber threats.

With the increased reliance on AI technologies, cybersecurity threats are becoming more prevalent. However, AI can also be harnessed to enhance cybersecurity measures, detecting and addressing potential vulnerabilities. As we enter 2024, the AI landscape will witness a renewed focus on data and security, ensuring the robustness and resilience of AI systems.

The Importance of Data Quality

Data quality plays a pivotal role in the success of AI projects. By leveraging accurate and comprehensive datasets, organizations can train AI models that provide valuable insights and drive informed decision-making. When developing an AI startup, prioritizing data quality ensures that the foundation of your AI solutions is reliable and capable of delivering meaningful results.

By employing data cleansing and data validation techniques, you can enhance the quality and integrity of your datasets. This entails identifying and rectifying errors, inconsistencies, and duplicates. Validating data ensures its accuracy, relevance, and completeness, resulting in more reliable AI models and outputs.

Enhancing Data Security

As AI systems become more prevalent, the security of data becomes a critical concern. Organizations must prioritize the implementation of robust security measures to protect sensitive information and maintain privacy. These measures encompass encryption, access controls, secure data storage, and authentication protocols.

Cyber threats pose significant risks to AI systems, as they can compromise data integrity, expose sensitive information, and disrupt operations. Mitigating these risks requires a holistic approach that includes regular vulnerability assessments, threat monitoring, and incident response plans.

Building Trust and Addressing AI Risks

Building trust in AI technologies is essential for their widespread adoption and acceptance. Organizations must be transparent about their data collection, usage, and storage practices. Implementing clear data protection policies and adhering to industry standards and regulations can enhance trust and foster confidence among users.

Additionally, addressing AI risks is crucial for maintaining the integrity and reliability of AI systems. Organizations must evaluate the potential biases, ethical considerations, and unintended consequences associated with their AI implementations. Regular audits, monitoring, and ongoing evaluation allow for timely identification and mitigation of risks.

Data QualityData SecurityAwareness of AI Risks
Ensure accuracy and reliability of datasetsImplement robust security measuresAddress biases and unintended consequences
Validate data for completeness and relevanceEncrypt sensitive dataRegular monitoring and evaluation
Cleanse data to rectify errors and duplicatesImplement access controlsComply with data protection regulations

Ensuring data quality, enhancing data security, and addressing AI risks are imperative as AI continues to shape various industries. By prioritizing these aspects, organizations can harness the full potential of AI technologies while safeguarding sensitive information and maintaining public trust. In 2024, expect to see a heightened focus and investment in data and security within the AI landscape.