Showing posts with label FutureOfAI. Show all posts
Showing posts with label FutureOfAI. Show all posts

Wednesday, October 1, 2025

Cursor Best Practices for Scalable and Efficient Code

 As engineering managers and developers, we often look for ways to speed up our workflows without sacrificing quality. Cursor, a code editor enhanced with AI capabilities, is one of those tools that, when used effectively, can be a true productivity multiplier. Over the last few weeks, I’ve been experimenting with it in real-world scenarios ;

building features, iterating on logic, and exploring new ideas.

Here’s a distilled set of practices that worked well.

1. Use Sonnet for Code-Heavy Tasks

Cursor offers different models, but I’ve found Sonnet particularly effective for code. It’s faster, more reliable, and better at understanding context when the task is purely programming-related. Of course, it comes with some extra $$

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2. Start a New Chat for New Features

When building something new, don’t clutter existing threads. A fresh chat keeps the scope clean, avoids accidental overwrites, and helps Cursor focus on the new feature instead of dragging in irrelevant context.

3. Provide Feedback — What Works, What Doesn’t

Cursor learns best when you guide it. If it suggests code that breaks existing logic or doesn’t align with your architecture, tell it. This prevents cascading mistakes and ensures the assistant builds on top of the right foundations.

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4. Reuse Context From Old Chats

When extending or refining an existing feature, bring in snippets or references from past chats. This helps Cursor understand continuity and prevents it from reinventing already-working code.

5. Use Ask Mode for Code Questions

Cursor provides two main modes: Agent and Ask. For generating code, Agent works fine. But when only asking precise code-related questions, switch to Ask Mode — it’s sharper and less verbose.

Pro tip: Ask mode uses relatively less tokens, so save those $$ for sonnet mode.

6. Always Tag Files for Clarity

When referencing code, tag files explicitly.

  • Example: @main.ts for the core logic
  • Example: Panel.tsx for feature listings

This helps Cursor focus on the right file and avoid mixing unrelated logic.

7. Review Code Midway & Iterate With File Names

Don’t wait until the end to review. Midway through, ask Cursor to refine specific files — mentioning them by name. Iteration with file-level precision reduces cleanup time and avoids surprises later.

8. Use @Web for Research

Instead of manually Googling, use Cursor’s @Web feature to pull in fresh information. It’s especially useful for comparing libraries, exploring API usage, or checking security considerations.

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9. Explore Official Docs With @Docs

When working with frameworks or libraries, @Docs is invaluable. Instead of scanning endless documentation pages, let Cursor fetch and summarize directly from official sources.

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10. Use Images for Layout Context

Cursor isn’t limited to text. You can drop screenshots or sketches of UI layouts, and it will translate them into code structure. This works great for dashboards, component alignments, or mobile screens.

11. Add .cursorrules in the Root Directory

One hidden gem: Cursor always listens to the .cursorrules file if it exists at the project root. Define conventions, dos and don’ts, and style preferences here. This ensures consistency without repeating instructions every time.

Final Thoughts

Cursor is not a magic wand — it’s a tool that shines when used with structure and intention. By setting clear boundaries, reviewing iteratively, and leveraging features like @Web@Docs, and .cursorrules, you can make Cursor a powerful coding partner for your team.

Used well, it won’t just save time; it will also elevate the quality of your codebase.

Last but not least, as the russian proverb goes, be it human or the code.

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Thursday, August 21, 2025

Model Context Protocol (MCP) and RAG: The Future of Smarter AI Systems


Model Context Protocol (MCP) is a new open standard that enhances AI models by enabling seamless connections to APIs, databases, file systems, and other tools without requiring custom code.

MCP follows a client-server model components:

  1. MCP Client: This is embedded inside the AI model. It sends structured requests to MCP Servers when the AI needs external data or services. For example, requesting data from PostgreSQL.
  2. MCP Server: Acts as a bridge between the AI model and the external system (e.g., PostgreSQL, Google Drive, APIs). It receives requests from the MCP Client, interacts with the external system, and returns data.

MCP vs. API: What's the Difference?

API (Application Programming Interface)

  • It’s a specific set of rules and endpoints that let one software system interact directly with another — for example, a REST API that lets you query a database or send messages.
  • APIs are concrete implementations providing access to particular services or data.

MCP (Model Context Protocol)

  • It’s a protocol or standard designed for AI models to understand how to use those APIs and other tools.
  • MCP isn’t the API itself; instead, it acts like a blueprint or instruction manual for the model.
  • It provides a structured, standardized way to describe which tools (APIs, databases, file systems) are available, what functions they expose, and how to communicate with them (input/output formats).
  • The MCP Server sits between the AI model and the actual APIs/tools, translating requests and responses while exposing the tools in a uniform manner.

So, MCP tells the AI model: “Here are the tools you can use, what they do, and how to talk to them.” While an API is the actual tool with its own set of commands and data.

It’s like MCP gives the AI a catalog + instruction guide to APIs, instead of the AI having to learn each API’s unique language individually.

RAG (Retrieval-Augmented Generation):

  • Vectorization Your prompt (or query) is converted into a vector—a numerical representation capturing its semantic meaning.
  • Similarity Search This vector is then used to search a vector database, which stores other data as vectors. The search finds vectors closest to your query vector based on mathematical similarity (like cosine similarity or Euclidean distance).
  • Retrieval The system retrieves the most semantically relevant content based on that similarity score.
  • Generation The AI model uses the retrieved content as context or knowledge to generate a more informed and accurate response.

RAG searches by meaning, making it powerful for getting precise and contextually relevant information from large datasets.


#AI #ArtificialIntelligence #ModelContextProtocol #MCP #MachineLearning #DataIntegration #APIs #AItools #TechInnovation #SoftwareDevelopment #DataScience #Automation #FutureOfAI #AIStandards #TechTrends

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