Prompt Engineering Techniques and Their Application in Low‑Code Development with GPT and LangChain

This article introduces the fundamentals of Prompt Engineering for large language models (LLMs) and demonstrates how these techniques can accelerate low‑code development.

It starts with the basic concepts of prompts, including the definition of a prompt, the role of instructions, context, input data, and output format. It then explains key prompt‑engineering tricks such as few‑shot prompting, chain‑of‑thought (CoT) prompting, and knowledge‑generation prompting, illustrating each with visual examples.

The article shows how to test prompts using OpenAI/ChatGPT APIs in both Node.js and Python, and discusses important parameters like temperature and top_p that affect the determinism and creativity of model outputs.

Role handling (system, user, assistant) and token management are covered, emphasizing the need to keep token usage within model limits and to calculate token costs.

Advanced techniques such as ReAct (Reasoning + Acting) are presented, where the model iteratively decides actions and thoughts, enabling integration with external tools.

LangChain is introduced as a powerful framework for building LLM‑driven applications. Its main modules—LLM wrappers, document loaders, prompt management, and chains—are described, and a quick example of building a GPT‑based web crawler with LangChain is provided.

The article then shifts to low‑code scenarios. It outlines how to use GPT to generate SQL from natural language (AI2SQL), how to construct API‑gateway prompts for AI‑friendly applications, and how to design DSLs for UI components, logic flow, and data visualization.

For data visualization, the workflow of generating chart DSLs, detecting anomalies, and selecting appropriate chart types is explained, with examples of using few‑shot and knowledge‑generation prompts to produce complete visual pages.

In the document‑system section, the use of embeddings and vector databases (e.g., pgvector, Supabase) is described to enable semantic search over large corpora. A sample SQL query is shown for similarity search:

SELECT * FROM items WHERE id != 1 ORDER BY embedding <-> (SELECT embedding FROM items WHERE id = 1) LIMIT 5;

The article concludes with a summary of best practices: keep prompts clear with instruction, context, input, and output format; leverage DSLs to bridge natural language and system actions; use knowledge‑generation and few‑shot prompting for domain‑specific knowledge; and employ vector databases to overcome token limits.