---
title: "Retrieval-Augmented Generation"
description: "34 posts about Retrieval-Augmented Generation, summarised, each linking to the original."
---

# Retrieval-Augmented Generation
> 34 posts about Retrieval-Augmented Generation, summarised, each linking to the original.

## Articles

### [A Practical Guide to LLM Fine Tuning](https://yomu.fyi/post/a-practical-guide-to-llm-fine-tuning.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 21, 2026
- Type: Tutorial

The guide presents LLM fine tuning as a way to adapt a pre-trained model to specific tasks, domains, or applications without full pretraining. It explains when prompting or few-shot learning is sufficient and when fine tuning is justified by quality gaps, domain terminology, latency, cost, or behavioral control. The recommended lifecycle covers scoping, data collection and cleaning, base-model and method selection, training, evaluation, deployment, and monitoring, with production-format consistency and held-out splits treated as important safeguards. It compares supervised and full fine tuning with parameter-efficient fine tuning (PEFT), including LoRA and QLoRA, which update fewer parameters and reduce compute and memory requirements. The main risks are noisy data, overfitting, and catastrophic forgetting; the proposed default is a pilot that compares a PEFT-adapted model with its base model, while combining fine tuning with RAG and prompt engineering when appropriate.


### [LLM apps: Real-world use cases, core building blocks and risks to know](https://yomu.fyi/post/llm-apps-real-world-use-cases-core-building-blocks-and-risks-to-know.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 21, 2026
- Type: Explainer

LLM applications are presented as production systems that extend beyond chatbots by connecting foundation models to external data, tools, APIs, memory and workflows. Modern categories include assistants and copilots, retrieval-augmented generation (RAG), autonomous agents, multi-agent systems and local or on-device inference, each with distinct capabilities and trade-offs. A typical RAG pipeline uses vector search over a curated knowledge base, then supplies retrieved passages to the model; chunking, source freshness and retrieval accuracy affect results. Production systems also require orchestration, evaluation, observability, governance, guardrails, audit trails and human-in-the-loop checkpoints, especially when agents can call tools or execute workflows. The source distinguishes prompt engineering, which guides behavior without changing model weights, from fine-tuning, which adapts models to domain-specific tasks, and describes a unified path combining data, models, retrieval, orchestration, evaluation and governance for production deployment.


### [What is pgvector?](https://yomu.fyi/post/what-is-pgvector.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 17, 2026
- Type: Explainer

pgvector is an open-source PostgreSQL extension that adds vector storage, indexing, and similarity search for embeddings alongside relational application data. It supports semantic search, recommendations, retrieval-augmented generation (RAG), image similarity, anomaly detection, and deduplication without requiring a separate vector database for many workloads. The extension uses the vector data type and distance metrics including L2, cosine similarity, and inner product, with HNSW and IVFFlat indexes offering different speed and memory trade-offs. Vector queries can also combine relational filters with Postgres full-text search for hybrid retrieval. pgvector is positioned as a practical choice for existing Postgres deployments, while pgvectorscale can extend its usefulness as data and query demands grow; dedicated vector databases become more relevant at larger scales, and Databricks AI Search serves complementary lakehouse workloads.


### [Building AI Search on Heroku](https://yomu.fyi/post/building-ai-search-on-heroku.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Anush DSouza
- Published: Jan 29, 2026
- Type: Tutorial

The reference architecture addresses a common RAG failure: vector search can return semantically similar documentation that does not answer the user's question. Its query pipeline retrieves the top 20 chunks with cosine similarity in Heroku Postgres and pgvector, then sends those candidates to Cohere Rerank 3.5, a cross-encoder reranker, before Claude 3.5 Sonnet generates an answer. The indexing pipeline crawls URLs, filters likely boilerplate, splits documents into 1,000-character chunks with 200-character overlap at natural boundaries, embeds them with Cohere Embed Multilingual, and batch-inserts them. Server-Sent Events stream embedding, search, reranking, sources, and generated text to avoid a blank screen during the multi-step request. The reranking stage can be made optional when lower latency matters more than precision.


### [SpellVault’s evolution: Beyond LLM apps, towards the agentic future](https://yomu.fyi/post/spellvault-s-evolution-beyond-llm-apps-towards-the-agentic-future.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Felix Haryanto Lie
- Published: Nov 21, 2025
- Type: Explainer

SpellVault began as Grab’s no-code platform for building LLM apps, using Retrieval-Augmented Generation (RAG) and plugins to connect applications with knowledge sources and external systems. Users could ground responses in Wikis, Google Docs, text, and PDFs, invoke custom HTTP endpoints, and access apps through the web, Slack, or APIs. The platform later added citations, Python execution for mathematical problems, scheduled actions, and drag-and-drop Workflows combining LLM calls, code, and Knowledge Vault lookups, while its executor moved toward graphs with nodes, edges, and states. SpellVault now exposes apps and RAG through MCP, allowing external agents and compatible clients to invoke apps and search, retrieve, or add Knowledge Vault information; TinyMCP adds MCP capabilities to FastAPI as a router.


### [How to build a Content Design Agent](https://yomu.fyi/post/how-to-build-a-content-design-agent.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Russell Norris
- Published: Oct 31, 2025
- Type: Tutorial

An overloaded solo content designer built VERBI, an AI content design agent, to scale standardized work without replacing human judgment or filling products with generic copy. The agent runs in Glean, using a curated collection of Intercom’s style guide, tone of voice, product and monetization glossaries, messaging guides, research, and official websites as retrieval-augmented generation sources. The system prompt defines its role, use cases, tone, and component rules, while a Glean Indexing API connection makes Storybook’s Surge design-system guidance available as a live source. Additional instructions tell VERBI to challenge users, avoid guessing when sources lack an answer, and keep responses concise. After 700+ triggers, the reported benefits are consistent content rules, faster drafts and iteration, fewer repetitive questions and errors, and more time for higher-impact design review.


### [Building Data-Aware AI Applications with Heroku AI and LlamaIndex](https://yomu.fyi/post/building-data-aware-ai-applications-with-heroku-ai-and-llamaindex.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Anush DSouza
- Published: Aug 19, 2025
- Type: Tutorial

Organizations building AI applications over private data must connect large language models with proprietary datasets without managing every infrastructure component. The post presents Heroku Managed Inference and Agents with LlamaIndex as a stack for retrieval-augmented generation (RAG), combining LlamaHub connectors, indexing, retrieval, and managed model services. The implementation provisions Heroku Postgres with pgvector, Claude 4 Sonnet for inference, and Cohere Embed Multilingual embeddings, then uses Python and LlamaIndex to load documents, store 1,024-dimensional vectors, and query them through an OpenAI-compatible embedding endpoint. PGVectorStore supplies the vector store, while Heroku environment variables provide service credentials and connection details. The result is a deployable pattern for querying private documents, with examples spanning financial analysis, insurance claims, customer support, and internal knowledge.


### [How to cut through AI hype and choose solutions with confidence](https://yomu.fyi/post/how-to-cut-through-ai-hype-and-choose-solutions-with-confidence.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Nicole Rashied
- Published: Aug 11, 2025
- Type: Explainer

Teams evaluating AI solutions can waste time, money, and trust when they lack technical fluency, choose unsuitable vendors, underestimate internal requirements, or expect immediate perfection. The post introduces Retrieval-Augmented Generation (RAG), vector search, agentic AI, and MCP as concepts that help teams distinguish static or rules-based tools from systems that retrieve current information, understand intent, take actions, and operate within governed boundaries. It argues that building an AI Agent in-house requires substantial work across embeddings, retrieval, prompt chaining, governance, security, maintenance, and model upgrades. Vendor evaluation should consider ongoing R&D, product roadmaps, adaptability, and maintenance, while implementation depends on structured content, documentation, process visibility, and APIs. Fin is cited as typically resolving over 76% of support questions out of the box, with resolution expected to improve through ongoing optimization and measurement.


### [Introducing the SOP-driven LLM agent frameworks](https://yomu.fyi/post/introducing-the-sop-driven-llm-agent-frameworks.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Fujiao Liu
- Published: Apr 25, 2025
- Type: Announcement

Grab introduces an LLM agent framework that uses Standard Operating Procedures (SOPs) to guide enterprise automation and improve consistency in complex processes. It represents SOPs as trees of action and decision nodes, while a planner uses Depth-First Search, prompt generation, and backtracking to direct an LLM-powered worker agent; context compression and limited APIs are used to address hallucination and efficiency concerns. An SOP editor, plugins for APIs, Python, and SQL, workflows, a GRAG pipeline, playground, multilingual user agent, and state stack extend the framework’s capabilities. Reported evaluations include over 99.8% accuracy, an Account Takeover Investigations bot automating 87% of cases and reducing investigation time from 23 minutes to three, and fraud investigations cutting AHT by 45% and saving over 300 man-hours monthly.


### [From RAG to Richness: How Ramp Revamped Industry Classification](https://yomu.fyi/post/from-rag-to-richness-how-ramp-revamped-industry-classification.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Ryne Carbone
- Published: Jan 15, 2025
- Type: Problem & solution

Ramp's former industry classification system combined third-party data, sales-entered information, and customer self-reporting, creating inconsistent, overly broad, and unauditable categories. Precise classification was needed for compliance, portfolio monitoring, sales targeting, and product analytics, despite fuzzy boundaries, sparse data, and no ground truth. Ramp migrated to six-digit NAICS codes and built an in-house Retrieval-Augmented Generation model that embeds business data, retrieves candidate codes, and uses an LLM to select a final prediction. The system evaluates retrieval with accuracy-at-k and final predictions with a hierarchy-aware fuzzy-accuracy metric, while ClickHouse stores knowledge-base embeddings and Kafka logs intermediate results. Deployed guardrails validate output codes, and the model has improved data quality, consistency, auditability, and control over tuning and costs.


### [More problems](https://yomu.fyi/post/more-problems.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Aman Sanger
- Published: May 25, 2024
- Type: Explainer

The post outlines research problems for the next phase of AI programming, centered on making coding systems predict actions, perform larger edits, understand codebases, and debug software. Proposed directions include low-latency next-action models for keystrokes, locations, files, terminal commands, and edits; background execution for “perfect edits” and pseudocode compilation; and multi-file changes supported by stronger tool use, reasoning, and agent trajectories. For context, retrieval alone is described as insufficient for codebase questions requiring multiple hops, while useful context may span documentation, source, history, UI pixels, logs, and messages. The post recommends combining retrieval, recurrence, and long-context attention, alongside specialized rerankers, multi-hop embedders, caching, and codebase-level retrieval. For reliability, it proposes calibrated bug detection, always-on AI linting, runtime-state tracking through cursor/debug, and improved debugger tool use, while noting trade-offs involving latency, false positives, infrastructure, and user experience.


### [Cutting Through the Noise: Three Things We've Learned About Generative AI and Developer Productivity](https://yomu.fyi/post/cutting-through-the-noise-three-things-we-ve-learned-about-generative.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: Senthil Padmanabhan
- Published: Feb 29, 2024
- Type: Explainer

eBay describes three tracks for generative AI developer productivity: adopting a commercial offering, fine-tuning an open-source LLM on internal data, and building a knowledge base. Because no single metric is reliable, the exercise combines surveys with Git, DORA, Flow, code-quality, and performance measures. In a 300-developer Copilot A/B test, the treatment group had 27% code acceptance, 17% faster pull-request creation-to-merge time, and 12% lower Lead Time for Change; Sonar quality was unchanged. eBayCoder, based on Code Llama 13B and trained on eBay code and documentation, is presented as useful for software upkeep and broader context. Its internal GPT uses retrieval-augmented generation to answer questions from distributed sources, but early results remain preliminary and nonsensical answers require feedback and RLHF.


### [How Yoko Li makes towns, tamagoes, and tools for local AI](https://yomu.fyi/post/how-yoko-li-makes-towns-tamagoes-and-tools-for-local-ai.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Jan 8, 2024
- Type: Explainer

Yoko Li’s local AI work spans playful agent experiments and practical tools for running language-model workflows on personal hardware. AI Town uses conversations among AI agents to produce changing relationships and emergent behavior, while AI Tamago feeds game state and user inputs to an unmodified LLaMA 2 7B model and uses Ollama’s JSON mode to generate pet actions. The Local AI Starter Kit packages document ingestion, vector storage, and language-model responses into a private, local “chat with a knowledge base” pipeline, avoiding setup across at least four API providers. The post also describes unreliable JSON generation, hallucinations, and grammar-guided workarounds, concluding that randomness is a liability for factual outputs but useful for game behavior and creative experimentation.


### [Go From 0 To 1 To Enterprise-Ready With MongoDB Atlas And LLMs](https://yomu.fyi/post/go-from-0-to-1-to-enterprise-ready-with-mongodb-atlas-and-llms.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Jun 22, 2023
- Type: Explainer

Generative AI applications require grounding in up-to-date operational data to avoid incorrect or uninformed answers, yet moving from prototype to enterprise-ready production is a significant challenge. MongoDB Atlas unifies operational, analytical, and vector search data services, enabling developers to provide long-term memory to large language models without costly fine-tuning. The post presents five resources for integrating MongoDB Atlas with LLMs, including tutorials for Atlas Vector Search with OpenAI, LlamaIndex, LangChain, MindsDB, and HuggingFace. These integrations support semantic search, chat applications with proprietary data, prediction generation, and automated reactions to data changes via serverless triggers. MongoDB Atlas offers a flexible document model, high performance, and scalability, validated by enterprises, to accelerate building production-ready generative AI applications.


[Newer posts](https://yomu.fyi/topic/retrieval-augmented-generation.md)