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🔹 To add an AI assistant prompt, click on the `AI Assistant` button in front of `Add Message By` to write an AI assistant prompt. It will open a text area with the role `AI Assistant`. Write an AI assistant prompt in this text area.

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🔹 For adding user prompts, click on the `User` button in front of `Add Message By` to write a user prompt. It will open a text area with the role `User`. Write a user prompt in this text area.

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❯❯ How to use these prompts? All of these prompts are present inside an AI agent pipe and can also be customized. These prompts can use variables that make them dynamic. 🔹 To add a system prompt, navigate to any of your Langbase pipes and click on the `System Prompt Instructions` text area.

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❯❯ User and AI assistant prompts A user prompt is a text input that a user provides to an LLM to which the LLM model responds. An AI assistant prompt is the LLM's generated output in response to a user prompt. It is the response that the LLM generates based on the user's input.

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❯❯ System prompt A system prompt sets the context, instructions, and guidelines for a language model like GPT-o1, Llama-3.2, etc., before it receives questions or tasks. It helps define the model's role, personality, tone, and other details to improve its responses to user input.

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❯❯ What is a prompt? A natural language text that requests the generative AI to perform a specific task. AI agent pipes on Langbase can contain system, user, and AI assistant prompts. This helps in creating desired serverless multi-agentic workflows.

❯❯ What is prompt engineering? Prompt engineering is the process where you guide generative AI solutions to generate desired outputs. It combines language skills, clear instructions, and trial-and-error to ensure AI produces high-quality outputs that align with user needs.

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All in on prompting AI agents. With Langbase, you can set 3 types of prompts to optimize AI agent performance: - System prompts - User prompts - AI assistant prompts Let's break it down 🧵

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❯❯ Prompt the LLM model That's it! You have successfully created a RAG system using a memory agent that can chat with your document. Now, you can prompt the LLM model to get answers to your questions. It will search the memory and provide you with the best possible answer to your question.

❯❯ Add memory to an AI agent 1. Create or open an AI agent pipe. 2. In the editor, click `Memory`. 3. Select the memory from the `Search Memory Sets` dropdown.

❯❯ Add document to memory 1. Save the document to be attached in the memory as a PDF or TXT. 2. Open the memory created and upload the file. 3. Click "Refresh" to check the upload status. 4. Once the status is `Ready`, the document is processed and ready to use.

❯❯ Get started 1. Signup on Langbase 2. Click on the `Memory` tab on the left sidebar to open the memory page. 3. Click on the `Add New` button. Enter a name for the new memory. 4. Click on the `Create` button to create the memory.

❯❯ What is RAG? LLMs are limited to pre-trained data—they can’t access real-time or private info. RAG (Retrieval-Augmented Generation) solves this by pulling specific, relevant data from external sources or databases, enabling your AI to respond with accuracy, not assumptions.

Turn any LLM—OpenAI, MetaAI, GoogleAI & more—into a scalable and serverless RAG system. And chat with your document using memory agents. In 4 simple steps, Build it! Details 🧵

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Reposted by Maham Codes

Last week in AI and agents was wild! - v0 runs full-stack apps - Google Gemini Memory - FLUX.1 Tools - Metadata support in Langbase Memory - Anthropic now adds Google Docs - GPT-4o creativity update - Mistral’s le Chat update - Agent Blocks in AutoGPT 🧵

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8️⃣ AutoGPT launched Agent Blocks: - Create simple AI agents - Combine them to solve complex tasks - Build on top of existing agents

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7️⃣ Mistral updated le Chat, their free AI assistant. It now offers: - Web search with citations - Tools for brainstorming, editing, and exporting - Document and image understanding (using Pixtral Large) - Image generation with Flux Pro

6️⃣ OpenAI updated the GPT-4o model, improving its creative writing for more natural and engaging results.

5️⃣ Anthropic dropped a cool new feature that allows you directly to add content from Google Docs to chats and projects.

4️⃣ Langbase introduced metadata support in their Memory agents. Helping to build real-time RAG with context and metadata.

3️⃣ Black Forest Labs released FLUX.1 Tools. ...a suite of models to add control to the base text-to-image model FLUX.1.

2️⃣ Google Gemini Advanced now includes Memory. That means users can save info by simply asking Gemini to remember it.

1️⃣ v0 dropped a list of different new features. One of the cool features is you can now build and run full-stack Next.js and React apps inside v0.

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How to Create an AI Memory in Langbase using API?

❯❯ Integrate with AI Agents You can attach AI memories to AI agent pipes. Each memory can hold multiple documents. You can create AI memory agent via the Langbase Studio or API. To create it using API check out this guide: langbase.com/guides/creat...

langbase.com

Learn to create an AI memory in Langbase using API.

❯❯ Core features 🔹 Semantic RAG: Context-aware search and retrieval beyond keywords. 🔹 Vector Storage: Hybrid search for large-scale data. 🔹 Internet Access: Combine private data with real-time info.

❯❯ How it works? 🔹 Ingest: Upload docs, files, and web content. 🔹 Process: Extract, embed, and build a semantic index. 🔹 Query: Retrieve context with queries. 🔹 Accuracy: Context-aware info reduces hallucination.

❯❯ AI Memory Agent AI Memory Agent at Langbase is a managed search engine as an API for developers. With it you can attach your private data to any LLM, minimizing hallucination. It acquires, processes, retains, and retrieves information for accurate responses.

❯❯ The problem with LLMs LLMs don't know anything about your private data. They are trained on public data and they hallucinate when when you ask them about your private data. This limitation makes it difficult for LLMs to provide accurate responses to your queries.

AI Memory Agents give AI tools context. and Langbase AI Memory agent is a serverless RAG solution that adds private data to any LLM, providing more context. Built with: 🔹 Vector storage 🔹 RAG 🔹 Semantic search 🔹 Smart chunking 🔹 Internet access More 🧵