#data indexing

[kariniai]

From Concept to Creation: Efficient RAG Systems

When creating a RAG (Retrieval Augmented Generation) system, you infuse a Large Language Model (LLM) with fresh, current knowledge. The goal is to make the LLM's responses to queries more factual and reduce instances that might produce incorrect or "hallucinated '' information.

A RAG system is a sophisticated blend of generative AI's creativity and a search engine's precision. It operates through several critical components working harmoniously to deliver accurate and relevant responses.

Retrieval: This component acts first, scouring a vast database to find information that matches the query. It uses advanced algorithms to ensure the data it fetches is relevant and current.

Augmentation: This engine weaves the found data into the query following retrieval. This enriched context allows for more informed and precise responses.

Generation: This engine crafts the response with the context now broadened by external data. It relies on a powerful language model to generate answers that are accurate and tailored to the enhanced input.

We can further break down this process into the following stages:

Data Indexing: The RAG journey begins by creating an index where data is collected and organized. This index is crucial as it guides the retrieval engine to the necessary information.

Input Query Processing: When a user poses a question, the system processes this input, setting the stage for the retrieval engine to begin its search.

Search and Ranking: The engine sifts through the indexed data, ranking the findings based on how closely they match the user's query.

Prompt Augmentation: Next, we weave the top-ranked pieces of information into the initial query. This enriched prompt provides a deeper context for crafting the final response.

Response Generation: With the augmented prompt in hand, the generation engine crafts a well-informed and contextually relevant response.

Evaluation: Regular evaluations compare its effectiveness to other methods and assess any adjustments to ensure the RAG system performs at its best. This step measures the accuracy, reliability, and response time, ensuring the system's quality remains high.

RAG Enhancements:

To enhance the effectiveness and precision of your RAG system, we recommend the following best practices:

Quality of Indexed Data: The first step in boosting a RAG system's performance is to improve the data it uses. This means carefully selecting and preparing the data before it's added to the system. Remove any duplicates, irrelevant documents, or inaccuracies. Regularly update documents to keep the system current. Clean data leads to more accurate responses from your RAG.

Optimize Index Structure: Adjusting the size of the data chunks your RAG system retrieves is crucial. Finding the perfect balance between too small and too large can significantly impact the relevance and completeness of the information provided. Experimentation and testing are vital to determining the ideal chunk size.

Incorporate Metadata: Adding metadata to your indexed data can drastically improve search relevance and structure. Use metadata like dates for sorting or specific sections in scientific papers to refine search results. Metadata adds a layer of precision atop your standard vector search.

Mixed Retrieval Methods: Combine vector search with keyword search to capture both advantages. This hybrid approach ensures you get semantically relevant results while catching important keywords.

ReRank Results: After retrieving a set of documents, reorder them to highlight the most relevant ones. With Rerank, we can improve your models by re-organizing your results based on certain parameters. There are many re-ranker models and techniques that you can utilize to optimize your search results.

Prompt Compression: Post-process the retrieved contexts by eliminating noise and emphasizing essential information, reducing the overall context length. Techniques such as Selective Context and LLMLingua can prioritize the most relevant elements.

Hypothetical Document Embedding (HyDE): Generate a hypothetical answer to a query and use it to find actual documents with similar content. This innovative approach demonstrates improved retrieval performance across various tasks.

Query Rewrite and Expansion: Before processing a query, have an LLM rewrite it to express the user's intent better, enhancing the match with relevant documents. This step can significantly refine the search process.

By implementing these strategies, businesses can significantly improve the functionality and accuracy of their RAG systems, leading to more effective and efficient outcomes.

Using Karini AI’s purpose-built platform for GenAIOps, you can build production-grade, efficient RAG systems within minutes. Reach out to us to discuss your use case.

[thinkingimages]

Erica BAUM

Untitled (Dragon-Flies) de la serie Frick, 1998 Tirage : éd. 1/6 + 2 EA

[noisytenant]

Being reminded once again that a lot of people have fucking sleeper cell agent triggers that make them instantly fail to see the human being in front of them, regardless of any personal history they have or any rapport. instantly, that person is an Enemy that cannot be reasoned with. Permanent fight or flight.

And that instead of this being seen as, you know, a rather maladaptive attitude to bring to your relationships that will permanently strip you of the capacity to experience full love and companionship, there is a dominant strain of thinking that this is a reasonable, righteous, moral good.

That a "boundary" looks like building an impenetrable wall that nobody can see but you; That conversation, negotiation, and collaboration aren't just avoided--They're treated with contempt. The very notion of trying to understand why another human being that you care about may suddenly act in an unpleasant or even monstrous way is spat upon and trampled underfoot. Complete abandonment is considered a first line of defense rather than a last resort.

I think we all need to do our best to get over this kind of thinking. And I don't mean that we should be push-overs; In actuality, moving away from this kind of rigid "boundary" often means advocating for yourself and fighting for what you think is right. I think we all deserve friends and allies who can compassionately challenge us when we adopt ways of thinking and behaving that hurt others without immediately assuming the worst.

[ghostofnuggetspast]

GhostOfNuggetsPast's Index Page

Here are links to the Tumblr versions of the things I made. Limericks, other poetry, fic rec posts, podfics, the "Sherlock & Co Podsters" and "Limericks are Legit" communities, etc.

(Latest update June 19, 2025)

The Limericks

Random Limericks, Bereft of Homes

2024 The Other Side is … Fandom? - Left-Overs (Dec 15) - It's Not a Bluff (Nov 29) - Manipulations with Sugar (Nov 19) - Hat (Nov 14) - A Septic Affair (Nov 6) - Doctor Love Meets His Nemesis (Nov 5) - You Thought I Couldn't See (Nov 5) - Not His First Meathead (Oct 7) 2025 Jonk-Blocked (Jan 28) - Old Friend (Jan 4) - A Warm Place (Jan 13) - Untitled (Feb 9) - Anatomy (Feb 18) - Oops (Feb 19) - Halftime (Mar 2) - Beware the Ides (Mar 14) - Look Here, Earl (Apr 1) - Escapism isn't Just a Magic Trick (Apr 13) - Unhand Me, Fiend (May 17) - Parenthood (May 27) - It's Raining Men, Kinda (Jun 14)

Collections

2024 May Prompts 2024 - Pride Month Sherlock & Co Prompt Fest! - Little Supermarket Bottles of Wine - Holidaze 2024 2025 YALC (Yet Another Limerick Collection) for January! - Martha’s Fluff Brew Remixed and Repoured: Magic, Mods, and Making Tea in the Face of Adversity (April 2 - 16) WIP Penguin Pop-ups (Nov 15, 2024 - ) Origin of This Penguin Storytime

WIP Johnlock Week 2025 (May 31 - ) Fight or ... Hug (May 31) - Figuring It Out (Jun 1) - Needs Gentling (Jun 2) - Again, With Feeling (Jun3)

Other Poetry

Random Not-Limericks

2024 Everyday Love (Nov 13) - No Key (Sept 19) - Three Bus Haiku Poems (Jul 15) 2025 Coat's Burma Shave (Feb 27) - The Cashier AU Thread (Mar19)

Collections

WIP Suspensoria and Other Poems by John H. Watson (Nov 19, 2024 - ) Ode to Your Hands Upon My Waking at 3AM to Hear the Violin An Ode to Your Orbicularis Oris Suspensoria A Lament for Your Stomach

Filks/Parodies/Podfics

2024 A Johnlock Jingle (Oct 24) - London Vice [podfic] (Nov 5) -Nightmare at Christmas (Dec 3) - There are Five Rings, Golden (Dec 4) - John, There's a Crime Outside (Dec 8) - Ugly Christmas Jumper Contest (Dec 9) - I Saw Three Kits (Dec 10) - The First Wedding Dance (Dec 13) - It's Beginning to Look a Lot Like Chocolate (Dec 16) - Fated Encounters [podfic] (Dec 23) - Red Hat Man (Dec 26) 2025 Pants on Fire [podfic] (Feb 25) - [Podfic] The Murder of Major Sayer (May 1) - [Podfic] Super Effective Against Ghost Types (Jun 19)

Community admin for

Sherlock & Co Podsters Limericks are Legit!

Gifts for me

2025 Birthday Card - Please Don't Text My Man (FTH) - I Dream of Sherlock (Summer Holmestice)

[aricastmblr]

Jimin Jungkook ocupan el octavo y décimo puesto en la categoría de Cantante del año en el “K-Brand Index” de la agencia de evaluación de big data Asia Brand Research Institute. — Periodo de investigación: 1 de enero de 2024 - 30 de noviembre de 2024

Jimin JungKook en Categorías de Cantante del año en el “K-Brand Index” de la agencia de evaluación de big data Asia Brand Research Institute

[chambersevidence]

Search Engines:

Search engines are independent computer systems that read or crawl webpages, documents, information sources, and links of all types accessible on the global network of computers on the planet Earth, the internet. Search engines at their most basic level read every word in every document they know of, and record which documents each word is in so that by searching for a words or set of words you can locate the addresses that relate to documents containing those words. More advanced search engines used more advanced algorithms to sort pages or documents returned as search results in order of likely applicability to the terms searched for, in order. More advanced search engines develop into large language models, or machine learning or artificial intelligence. Machine learning or artificial intelligence or large language models (LLMs) can be run in a virtual machine or shell on a computer and allowed to access all or part of accessible data, as needs dictate.

[chekovbeforeyouwrekovyourself]

If I’m not analyzing, categorizing, codifying or otherwise collating, I might as well be dead.

[nerice]

i love that instagram eu lets you opt out of ai training. i'm still deleting my acct bitch show me zuckerberg's head on a stake or ima do it myself

[excelhelps]

How to use the powerful INDEX MATCH functions in Excel

[androdragynous]

I was put on this earth to be meticulously keeping record of old books and lost media. I know this in my heart

[villainessbian]

"I'm your arch's nemesis"

Normal people: what? that doesn't sound right, don't you mean-

Architects: HAVE AT THEE, FUCKING BUTTRESS

[atompowers]

🌞 3 Treemendously Simple Sustainable City Living Scoring Tools

[infoanalysishub]

Why Your Best Content Is Invisible to AI Search Engines

Discover why your top-quality content may be hidden from AI search engines and learn practical fixes to boost visibility and traffic. Why Your Best Content Is Invisible to AI Search Engines—And How to Fix It In the age of AI-powered search engines like Google’s SGE, Bing AI, and OpenAI’s ChatGPT plugins, creating exceptional content is only half the battle. The other half? Making sure it’s…

[haysaprocky]

when i was in elementary school i used to ask my teachers for extra homework. and i think as an adult my bosses can smell that on me because they really think im superwoman or a renaissance man or something

[10bmnews]

China's consumer prices fall for third month amid ongoing economic struggles - Times of India

Representative image (Picture credit: ANI) Consumer prices in China fell for the third month in April as the country grapples with sluggish spending amid a fierce trade war with the United States.The latest data, released on Saturday by the National Bureau of Statistics (NBS), showed that the consumer price index (CPI), a crucial inflation measure, dropped by 0.1 percent year-on-year. This marks…

[deep-definition]

Why Google May Not Show Your Knowledge Graph Information

Discover the common reasons why Google may not show your Knowledge Graph information and how to fix it. Learn about authority, schema markup, local SEO, and more to boost your visibility. Why Google May Not Show Your Knowledge Graph Information Why Google May Not Show Your Knowledge Graph Information Google’s Knowledge Graph is a powerful tool. It enhances search results by displaying…