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Your AI agent doesn't need to be hacked to fail — sometimes a single sentence is enough. In this video, I break down the first of three guardrail layers every production AI agent needs, and show you exactly how to detect and block the most common input-side failures using Python.
Pinecone Nexus makes agents more accurate, faster, lower cost, and trusted, outperforming agents that use frontier models alone on Sierra’s agentic work benchmark
Is RAG Dead? The Role of Vector Search in AI Efficiency When Anthropic came out with a context window of 100K tokens, they said: “Vector search is dead. LLMs are getting more accurate and won’t need RAG anymore.” Google’s Gemini 1.5 now offers a context window of 10 million tokens.
This notebook demonstrates how to setup breakpoints in a Haystack pipeline. In this case, we will set up break points in a hybrid retrieval-augmented generation (RAG) pipeline.
Hexabot 3.4.0 rebuilds RAG around database-backed helpers, with full-text and vector retrieval options plus workflow and admin improvements.
A practical guide to eight embedding models — text-embedding-3-small/large, gemini-embedding-001, embeddinggemma-300m, qwen3-embedding, and more.
Level: Advanced Time to complete: 20 minutes Components Used: Agent, ChatPromptBuilder, InMemoryBM25Retriever OpenAIChatGenerator Archived Components Used: InMemoryChatMessageStore, ChatMessageRetriever, ChatMessageWriter Prerequisites: You need an OpenAI API Key Goal: After completing this…
Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline’s strengths and weaknesses using LLMs or ground-truth labels.
by Tuana Celik ( LI, Twitter/X) This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval cookbook & full article 2️⃣ Query Expansion & the full article 3️⃣ Query Decomposition cookbook & full article 4️⃣ Automated Metadata Enrichment In this…
In this cookbook we will show how to calculate a hallucination risk based on the research paper LLMs are Bayesian, in Expectation, not in Realization and this GitHub repo, https://github.com/leochlon/hallbayes. In this notebook, we’ll use the OpenAIChatGenerator from haystack-experimental.
When it comes to AI impact, it assists informational queries but not transactional ones. Make sure your content is eligible to be cited by AI and use long-tail keywords to stay visible. The post AI Overview Optimization: Get Content Into Google AI Answers appeared first on ACME.BOT.
#RAG #AIEngineer #AIRoadmap2026 🔗 Join the WhatsApp Community https://chat.whatsapp.com/ENnDGZ41lMYBHCApJIpzo6?mode=gi_t This is to bring together serious and like-minded learners who want to learn AI, share useful resources, discuss ideas, and grow together.
Learn how to set up and utilize the MCP Documentation Server to streamline your documentation workflows, improve collaboration, and support AI-driven projects with detailed, step-by-step guidance.
Flowise AI review: Features, pricing, pros & cons, and top 2026 alternatives like Voiceflow, Langflow, and n8n for building smart AI agents.
Prevent LLM hallucinations with 5 key strategies. Learn how RAG, human feedback, and guardrails help improve accuracy in large language models.
Vector search and graph traversal in a single SurrealDB query. How single-engine hybrid retrieval makes RAG agents more accurate.
AI chatbots don't get answers wrong because the model got worse. They get answers wrong because the knowledge base moved on and nothing told the retrieval index. Here's what causes stale answers, how to catch them, and the sync mechanisms that actually fix it.
Managed Weaviate is now in public preview on DigitalOcean, offering an easy way for developers to run Weaviate in production, at a price that makes sense from day one.
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