#rag Development Tutorials & Tips | Roast Dev

100daysofcode 100daysofdevops 100pay 10mistakes 10yearworkanniversary 11 11labs 11tly 11ty 127001

Understanding Reciprocal Rank Fusion (RRF) in Retrieval-Augmented Systems

In our last discussion, we explored how Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by fetching external information to improve their responses.Today, let's dive deeper ...

26.04.2025 0 Read More

Part 2: Why Your AI is Stuck in a Memento Loop

In Part 1, we likened today's typical AI "memory" to the plight of Leonard Shelby in Memento: brilliant at accessing isolated fragments (Polaroids, notes, tattoos) but unable to weave them into the co...

26.04.2025 0 Read More

Guardrails as Architecture: Safe guarding GenAI apps

In the rapidly evolving landscape of generative AI applications, Retrieval Augmented Generation (RAG) has emerged as a powerful paradigm for building more reliable and knowledge-grounded systems.While...

27.04.2025 0 Read More

AutoRAGLearnings: Hands-On RAG Pipeline Tuning with Greedy Search

If you’ve ever spent hours tweaking a Retrieval-Augmented Generation (RAG) pipeline—wondering whether BM25 or a vector index works better, or if duplicating a passage in your prompt helps—AutoRA...

27.04.2025 0 Read More

How run LLM in local using Docker.

Self-hosted LLMs are gaining a lot of momentum. They offer advantages such as improved performance, lower costs, and better data privacy. You don't need to rely on third-party APIs, which means no une...

27.04.2025 0 Read More

How to train LLM faster

RAG is an architecture where an LLM (Large Language Model) is not asked to answer purely from its own parameters but is given external information at runtime to use while answering.✅ Goal: Reduce ...

28.04.2025 0 Read More

Building a Prompt-Based Crypto Trading Platform with RAG and Reddit Sentiment Analysis using Haystack

In today's fast-paced crypto market, having data-driven insights can make the difference between profit and loss. In this blog post, I'll walk you through a project that combines natural language proc...

28.04.2025 0 Read More

Configuring your own deep research tool (Using Nix Flakes)

So, you've come across deep search tools and are wondering if you can build your own. Well, you certainly can! Building one yourself might involve tasks like scraping web URLs, implementing your own R...

28.04.2025 0 Read More

Part 3: How TrustGraph's Knowledge Cores End the Memento Nightmare

In Parts 1 and 2, we exposed the dangerous flaw in most current AI "memory": like Leonard Shelby in Memento, our systems often operate on disconnected fragments, unable to form the interconnected know...

28.04.2025 0 Read More

Retrieval Metrics Demystified: From BM25 Baselines to EM@5 & Answer F1

“If a fact falls in a database and nobody retrieves it, does it make a sound?” Retrieval‑Augmented Generation (RAG) lives or dies on that first hop—can the system put the right snippets in fro...

29.04.2025 0 Read More

Understanding Reciprocal Rank Fusion (RRF) in Retrieval-Augmented Systems

Part 2: Why Your AI is Stuck in a Memento Loop

Guardrails as Architecture: Safe guarding GenAI apps

AutoRAGLearnings: Hands-On RAG Pipeline Tuning with Greedy Search

How run LLM in local using Docker.

How to train LLM faster

Building a Prompt-Based Crypto Trading Platform with RAG and Reddit Sentiment Analysis using Haystack

Configuring your own deep research tool (Using Nix Flakes)

Part 3: How TrustGraph's Knowledge Cores End the Memento Nightmare

Retrieval Metrics Demystified: From BM25 Baselines to EM@5 & Answer F1

#reading

#popular