The Science Behind RAG and Qlik Answers
- Igor Alcantara
- 5 days ago
- 17 min read
What if your enterprise data had a subconscious? A deep, multilayered repository of memories. Some crisp and current, others buried in old documents or silos, waiting to be awakened. What if, instead of going through dashboards and PDFs, you could simply ask a question and get an intelligent, verified answer?
Welcome to the world of Retrieval-Augmented Generation, or RAG, and welcome to the science behind Qlik Answers! We recently release an episode of the Data Voyagers Podcast about RAG. You can listen here (also, make sure to subscribe to it in your favorite podcast or audio app). This article is intended to give you a mode detail walkthrough of this technology. The better you know, the better you can use, avoid traps, and reduce risks.
To understand how this all works, let’s take a ride through the dream architecture of Inception, where the deeper you go, the more abstract, but meaningful, things become. That’s not far from how RAG and vector databases work under the hood. Before we begin, there are a couple of disclaimers I need to make: first of all, I do not have access to Qlik Answers source code, therefore I am making assumptions based on industry standards and best practices. Secondly, this is a long article, but trust me, it is worth the read. I will make it as simple as possible without giving up the deep dive it deserves.
Let’s go layer by layer.
1: What is RAG, Really?
At its core, Retrieval-Augmented Generation (RAG) is a composite architecture that combines two distinct but complementary AI capabilities:
Information Retrieval, responsible for pulling relevant content from a knowledge base based on semantic similarity.
Natural Language Generation, responsible for composing an answer using that retrieved content.
This pairing addresses one of the most critical limitations of large language models: they don’t know what they don’t know. LLMs are trained on a massive but static dataset. Once deployed, they do not have access to updated content or specific proprietary knowledge, like your company’s latest compliance policy or product pricing manual. Without help, they’ll either dodge the question or (more dangerously) fabricate an answer that sounds right but is totally wrong. The latest versions of tools like Perplexity, ChatGPT and others have web search, but this is also not curated, therefore, is pruned to fabricate false statements. That is how a common LLM model, like ChatGPT or Gemini work. RAG fixes that. Here’s how, step by step, in technical terms. There are a few terms I will mention a few times, like Transformers or Embedding. If you want to understand them better, please read my article about how LLMs work here.
Step 1: Preprocessing and Embedding
Before the model can respond to any questions, the knowledge base must be prepared:
Document Chunking: Each unstructured document (PDF, HTML, Word doc, etc.) is split into manageable chunks, usually paragraphs or sections, using natural language-aware segmentation. This balances context preservation with token limits. A chunk is basically the smallest unit of text information possible. How a text is converted to chunks varies depending on the algorithm, but for example, the word understandable might generate 3 chunks: the root, the prefix, and the suffix: stand, under, able.
Vectorization: Each chunk is converted into an embedding using a sentence transformer: a neural network that encodes text into high-dimensional numerical vectors. These embeddings represent the semantic meaning of each chunk, not just its words.
Storage: These vectors are stored in a vector database like FAISS, Pinecone, or OpenSearch, Qlik’s vector storage tool. Alongside each vector, metadata is stored: source filename, location in the document, tags, timestamps, etc.
This is the “retrieval memory” layer, a numerically indexed, semantically searchable snapshot of your organization's knowledge.
Step 2: User Query Handling
When a user types a question into the chat assistant, it goes through a similar process:
The question is embedded using the same sentence transformer as used during preprocessing.
This vector is then compared to the document vectors in the database using a similarity metric (typically cosine similarity).
The top N (e.g., 5 to 10) most similar document chunks are retrieved, these are assumed to be contextually relevant to the user’s question.
This step is purely about finding content that semantically aligns with the intent behind the user’s prompt. It does not involve generation or interpretation yet.
Step 3: Retrieval-Informed Prompt Assembly
Now that the most relevant pieces of content have been pulled, they are bundled together into a prompt that will be sent to the LLM. This prompt typically includes:
A system instruction (e.g., “Answer the user’s question using only the content below.”)
The retrieved passages with source information
The user’s original question
This technique is sometimes called Context Injection or Grounding, and it’s the core of the RAG strategy.
Example structure:
System: You are a helpful assistant. Use the context below to answer the user's question truthfully.
Context:
[Source: finance_q4_report.pdf]
"Revenue dropped by 18% due to currency headwinds and delayed enterprise renewals..."
[Source: marketing_strategy_march.html]
"We launched a targeted campaign in late March to recover from Q1 performance slippage..."
User: Why did revenue decline in Q1?
Step 4: Generation by the LLM
This assembled prompt is passed to a large language model, in the case of Qlik Answers, Anthropic Claude.
Here’s what happens inside the model:
Claude scans the context and identifies which parts align with the question
It builds a semantic answer, pulling phrasing, facts, and reasoning from the provided chunks
Crucially, because the prompt contains only trusted, internal content, Claude is not free to wander. If the retrieval doesn’t support a particular detail, it won’t invent one.
This guards against hallucination, the LLM’s habit of guessing when it doesn’t know. More about this later in this article.
Step 5: Output + Source Attribution
The final output includes:
A fluent, human-readable answer
Citations pointing to the documents and sections that contributed to the answer
From the user’s perspective, they just asked a question and got a concise, evidence-backed answer. From a systems perspective, they just triggered a mini orchestration of:
Embedding
Retrieval
Prompt engineering
Controlled generation
Source tagging
All in under a few seconds.
2: Embeddings: The Compressed Memories
If Retrieval-Augmented Generation is a dream machine, embeddings are how memories are encoded. They are the mechanism that transforms messy, unstructured human language into a form that machines can search, compare, and reason about. Embeddings are one of the most powerful ideas in modern AI. Let’s unpack them.
What Is an Embedding?
An embedding is a numerical vector, usually a dense array of floating-point numbers that captures the semantic meaning of a piece of text. Instead of focusing on individual words or syntax, embeddings aim to encode what the text is trying to say.
You can think of it as translating text into coordinates in a high-dimensional space, usually between 384 and 1536 dimensions, depending on the model.
For example:
“The revenue dropped in Q1 due to lower demand.”
“Sales declined early in the year because customer activity slowed.”
These two sentences have very different words, but very similar meaning. When embedded, they will produce vectors that are close together in this high-dimensional space.
This is what makes semantic search possible. When I say close, I mean mathematical close. We can use techniques like Euclidian Distance or Cosine Distance (my go-to for text difference).
How Are Embeddings Created?
Embeddings are generated by transformer-based neural networks, typically fine-tuned on large corpora for sentence similarity tasks. Common models include:
OpenAI's text-embedding-ada-002
Hugging Face’s sentence-transformers
Cohere’s multilingual embedding models
Anthropic’s Claude embedding APIs.
The process looks like this:
Tokenization: The input text is broken into tokens (words or subwords).
Vector Transformation: These tokens pass through transformer layers (attention-based), where their relationships are learned.
Pooling: The final embedding is computed from one or more token representations (typically via average pooling, mean/max, or CLS token).
The result is a single vector per text chunk that captures meaning. It can be represented in multi layers, each layer giving a different semantic perspective. You can read my article about the Attention and Modern AI for more details.
Why Embeddings Work Better Than Keywords
Traditional search is based on keyword overlap:
If you type “Q1 sales decline,” a search engine looks for documents with the exact words “Q1,” “sales,” and “decline.”
That fails when:
The document says “first quarter” instead of “Q1”
The term “drop in revenue” is used instead of “decline in sales”
Embeddings solve this by encoding meaning, not just syntax. In embedding space:
“Decline in sales” is close to “fall in revenue”
“Q1” is close to “first quarter”
“Customer slowdown” is near “reduced demand”
This makes semantic retrieval possible: finding content about your query, not just containing the same words.
Chunking and Embedding in Practice
Before creating embeddings, documents are broken into chunks. Why? Firsy, because LLMs and embedding models have a token limit: they can’t process entire documents at once. Secondly, semantic granularity improves when chunks are focused (e.g., one paragraph or section).
Typical chunking approaches:
Paragraph-based (e.g., each section separated by line breaks).
Fixed-size token windows (e.g., 500 tokens with 100-token overlap).
Adaptive chunking using sentence boundaries and heuristics.
Once chunked, each section is embedded separately and stored with its associated metadata.
Example:
Chunk Text | Embedding | Metadata |
"In Q1, revenue dropped by 15% due to macroeconomic factors." | [0.1, -0.08, 0.23, ...] | file: revenue2025.pdf, page: 4 |
What Does “Closeness” Mean in Embedding Space?
The distance between embeddings indicates semantic similarity. Common distance metrics include:
Cosine Similarity: Measures the angle between two vectors (ignores magnitude). Widely used in RAG.
Dot Product: Effective when vectors are normalized.
Euclidean Distance: Less common for high-dimensional embeddings due to scaling issues.
In Qlik Answers, cosine similarity is typically used to rank how closely a document chunk matches the user’s question.
Score closer to 1.0 = highly relevant
Score near 0.0 = irrelevant
The top N scoring chunks (usually 3 to 10) are selected for prompt construction.
3: The Vector Database: Your Organized Subconscious
If embeddings are the compressed memories of your documents, then the vector database is the brain’s hippocampus, the subsystem responsible for storing and retrieving those memories quickly, efficiently, and in context.
You can’t ask a question to a language model and expect semantic grounding unless there’s a system in place that can rapidly surface the right fragments of meaning from a sea of possibilities.
This is where the vector database enters the Retrieval-Augmented Generation (RAG) pipeline. It’s a cornerstone component of Qlik Answers’ architecture.
What is a Vector Database?
A vector database is a specialized system designed to store and retrieve vector embeddings. These are the high-dimensional numerical representations of text that encode meaning.
Unlike traditional relational databases, where search is based on exact values or indexes, vector databases allow for approximate nearest neighbor (ANN) search. This means instead of looking for an exact match, they retrieve items that are semantically similar to the query vector based on distance in vector space.
This is a fundamental shift: you’re not querying for “the word ‘revenue’ on page 4,” but rather “things that mean something close to revenue decline in Q1.”
Core Components of a Vector Database
Let’s go deeper. A vector database has a few key architectural components:
Indexing Engine: This component organizes the high-dimensional vectors so that similarity search is fast. It often uses ANN algorithms like:
HNSW (Hierarchical Navigable Small World Graphs): Supports efficient graph-based traversal for ultra-fast recall.
IVF (Inverted File Indexes): Partitions the space into clusters, speeding up search within each subset.
PQ (Product Quantization): Compresses vectors for large-scale storage with lower precision.
Metadata Store: Alongside each vector, the system stores metadata:
Document source
Chunk position (page, paragraph)
Tags (e.g. department, type, owner)
Timestamp or version control markers
This allows you to not just find the right passage, but also filter results based on organizational rules.
Query Engine: Given an embedded question, this engine calculates the similarity (usually cosine similarity) between the query vector and all indexed vectors or a narrowed subset, depending on filter conditions.
Hybrid Filters and Routing (optional): In more advanced systems (like what Qlik is moving toward), the vector DB can combine semantic filtering with symbolic constraints:
“Only search embeddings from documents tagged with 2024”
“Exclude sources labeled as drafts”
“Prioritize sales over HR content”
The Retrieval Process: Step-by-Step
When a user types a question into Qlik Answers, here’s what happens behind the scenes in the vector database:
Embedding the Query: The question is transformed into a vector using the same embedding model used during document indexing (to ensure alignment in vector space).
ANN Search: The query vector is passed into the vector database, where an ANN algorithm compares it against stored vectors to find the most similar ones.
Top-K Retrieval: The engine retrieves the top K closest chunks, where K is configurable (usually between 3–10 depending on use case and model context limits).
Metadata Filtering: Optional filters may apply (e.g., role-based access control, date ranges, source types). This is especially critical in enterprise systems to ensure relevance, accuracy, and compliance.
Result Preparation: Each retrieved result is not just text but a fully enriched object:
The original passage
The similarity score
The source filename or app name
The page or paragraph reference
Any applicable tags (e.g. “Author: CFO”)
Output to Generator: These top-k passages are passed to the LLM (Claude, in Qlik’s case) in the prompt payload.
This system turns millions of disorganized fragments into a ranked, explainable shortlist of relevant context.
The Latency Challenge: Why ANN Matters
Searching millions of 1536-dimensional vectors in real-time is computationally expensive. That’s why vector DBs lean heavily on ANN (Approximate Nearest Neighbor) rather than brute-force search.
While ANN gives up some precision, it offers orders-of-magnitude performance gains, typically retrieving relevant context in under 100ms at scale. This makes live, conversational applications like Qlik Answers not only feasible but performant.
Real-World Example: Revenue Query
Let’s say a user asks:
“What caused our Q1 revenue decline in LATAM?”
Here’s what the vector database does:
The question is embedded
ANN search finds top matching chunks like:
“Revenue in LATAM dropped 18% in Q1 due to currency devaluation” (finance_report_q1.pdf)
“The LATAM market was affected by import tariffs starting in January” (region_notes_jan2025.docx)
These chunks, along with their metadata, are returned and injected into Claude's context window for generation
The answer cites both sources and links back to the originals
The Qlik Answers Approach
Qlik Answers uses OpenSearch as its vector database engine, a powerful open-source platform originally derived from Elasticsearch that now includes robust support for high-performance vector similarity search. OpenSearch enables Qlik to store millions of document embeddings, each representing a semantically rich chunk of unstructured data, and retrieve them in milliseconds using approximate nearest neighbor (ANN) algorithms like HNSW (Hierarchical Navigable Small World graphs).
This is crucial for scaling Retrieval-Augmented Generation (RAG) to enterprise levels, where users expect fast, accurate responses even when querying against hundreds of thousands of documents.
What sets OpenSearch apart in this architecture is its hybrid flexibility. Not only does it enable semantic search via vectors, but it also integrates seamlessly with traditional keyword indexing and structured filters, allowing Qlik to apply fine-grained metadata controls, such as document type, author, timestamp, data classification, and versioning, during retrieval. This is essential for enterprise governance, as Qlik Answers must ensure that only the most relevant, up-to-date, and authorized content is retrieved for each user query. Not all of these functionalities are currently implemented in Qlik Answers but the capability is there. Give it time and it will become a feature.
Moreover, OpenSearch’s multi-tenant capability, security model, and compatibility with role-based access control (RBAC) make it an ideal backbone for knowledge retrieval in regulated environments. Combined with Qlik’s orchestration layer, this infrastructure turns OpenSearch from a search engine into a semantically aware, real-time index of enterprise knowledge, enabling Qlik Answers to be not just fast, but trustworthy and auditable.
4: Generation: Synthesizing the Answer
Once the vector database has retrieved the most semantically relevant chunks of text in response to a user query, it’s time for the real magic to happen: natural language generation. This is where a large language model (LLM) like Claude from Anthropic enters the stage, and transforms disconnected memory fragments into a coherent, fluent, and relevant response.
But this isn’t just next-word prediction. It’s a carefully orchestrated, multi-layered process designed to stay grounded in the retrieved content, follow instruction patterns, and respect enterprise constraints. Let’s break it down.
Step 1: Prompt Construction
The first critical task before generation even begins is to construct the prompt — the full message sent to the LLM that contains:
A system instruction or directive
The retrieved document chunks, including source metadata
The user’s question
This prompt is often formatted in a structured layout to guide the model's behavior. For example:
System:
You are a helpful assistant answering questions based only on the context provided below. Do not answer questions if the information is not present in the context.
Context:
[Source: compliance_policy_2024.pdf, Page 3]
"Night shift employees must complete safety drills quarterly, with documentation submitted by HR."
[Source: operations_manual.docx, Section 4.2]
"Due to recent incidents, after-hours staff must check in every 2 hours with security."
User Question:
What are the compliance requirements for night shift employees?
This structure anchors the model and encourages instruction fidelity: the ability to follow precise directions, rather than improvising or free-associating.
Step 2: Window Management, Token Limits and Chunk Optimization
Claude (like other LLMs) has a token limit: a maximum number of tokens it can process at once. For example, Claude 2 and 3 can handle up to 100k tokens depending on the implementation tier.
To fit within this limit, Qlik Answers must manage:
The number and size of retrieved context chunks
The length of the user’s query
The system instruction overhead
If the total exceeds the token limit, the orchestration layer must rank and truncate the input:
Rank by cosine similarity or retrieval confidence score
Drop lower-ranked chunks
Or compress/summarize lower-priority content using pre-generation tools
This context optimization ensures the model receives only the most useful and targeted information, maximizing relevance and minimizing noise.
Step 3: Controlled Generation with Claude
With the prompt constructed and optimized, the system calls the Claude API, passing in the full message.
Here’s what happens inside the model:
Token Encoding: Claude tokenizes the input prompt, converting it into numerical IDs the model understands.
Transformer Processing: Claude uses attention-based transformer layers to process the prompt. Each layer allows the model to “pay attention” to relationships between tokens across the entire prompt, understanding how the user’s question relates to each context chunk.
Grounded Answer Generation: Crucially, Claude generates the answer based on patterns learned during training and the constraints of the prompt.
Because the instruction clearly states:
Only use provided context
Do not guess if the answer is not found
Claude operates in a restricted generation mode, often called closed-book question answering. This prevents it from inventing content, even if it might have seen something similar during training.
Sampling and Decoding: Claude samples the most probable next token using methods like:
Greedy decoding (take the highest probability token at each step)
Top-k sampling (restrict choices to top-k probable tokens)
Nucleus sampling (sample from a dynamic set of top tokens whose cumulative probability exceeds a threshold).
Qlik Answers typically configures this generation to favor determinism and faithfulness to source, rather than creativity.
Step 4: Answer Post-Processing
Once the LLM returns the generated answer, Qlik Answers applies post-processing steps:
Source Linking: It maps segments of the answer back to the retrieved document chunks that contributed to them, enabling source attribution.
Formatting: The answer is structured for readability (short paragraphs, bullet points, or summaries).
Citations: Qlik Answers displays a list of the source documents (with filenames or friendly titles) used in the answer. In some implementations, it highlights or links to the exact section within the document.
Fallback Handling: If no relevant chunks were retrieved, and the model’s response indicates uncertainty, Qlik Answers will say something like:
“I don’t have enough information to answer that question based on the current knowledge base.”
This ensures transparency, verifiability, and a strict no-hallucination policy: which is critical in enterprise and regulated environments.
5: Qlik Answers
RAG is a powerful architecture but making it enterprise-ready requires more than combining a vector database and a language model. It requires orchestration, security, governance, and scale. Qlik Answers delivers this by turning RAG from a research paper into a production-grade, compliant, and explainable assistant for the business world. Even better, it is plug-and-play. No code is required.
This is not a wrapper around a public chatbot like most of new Silicon Valley startups these days. Qlik Answers is a deeply engineered solution that uses best-in-class components to bring retrieval-grounded AI into the enterprise with safety, traceability, and performance at its core. Let’s break down what that looks like under the hood.
The Technical Stack Behind Qlik Answers
Qlik Answers brings together a distributed, multi-layered architecture using the following technologies:
Temporal handles distributed workflow orchestration during the ingestion process. When documents are uploaded or refreshed, Temporal coordinates the parsing, chunking, embedding, and indexing steps as scalable, fault-tolerant microservices.
OpenSearch is the vector database and semantic search engine. Document chunks are embedded into high-dimensional vectors and stored in OpenSearch, which then enables efficient approximate nearest neighbor (ANN) search to retrieve the most relevant pieces of content during query time.
Cohere Rerank, hosted on Amazon SageMaker, is applied after retrieval. It takes the top-k candidates from OpenSearch and reorders them by semantic relevance to the query using a fine-tuned reranker model. This improves the quality of the final prompt fed into the LLM, helping mitigate irrelevance and noise.
AWS Bedrock is used to connect to Anthropic Claude, the LLM responsible for synthesizing final answers. Bedrock allows Qlik to operate Claude through a secure, enterprise-grade API, keeping all prompts and responses within the boundaries of Qlik’s cloud governance.
This modular architecture ensures flexibility, reliability, and clear separation of responsibilities, embedding, retrieval, reranking, generation, each optimized by specialized tooling.
Enterprise-Grade Guardrails and Safety Systems
Where many RAG systems stop at “get relevant content, generate text,” Qlik Answers adds several layers of defense to ensure trustworthy, safe, and governed output:
1. PII and Secrets Sanitization
During preprocessing and query time, Qlik Answers scans both content and user input for personally identifying information (PII) or sensitive credentials.
Detected elements are removed before sending input to the LLM, preventing any risk of leakage to the model.
After the model returns its response, the sanitized items are reinserted into the final output, preserving readability without compromising privacy.
2. LLM Monitoring
Qlik Answers continuously monitors LLM responses for toxic, biased, or harmful content using post-processing validators.
If responses fail safety checks, they are flagged, blocked, or rewritten based on policy controls.
3. Hallucination Mitigation
The generation engine is retrieval-gated: if relevant context isn’t retrieved, the LLM does not answer. It will instead respond with an appropriate fallback such as:
“I'm sorry, I don't have enough information to answer that question.”
Additionally, a contextual alignment layer ensures that even if documents are retrieved, the generated answer is semantically aligned with them.
Users can view the exact source content that was used to generate the response, maintaining auditability and trust.
4. Prompt Injection Defense
Qlik Answers inspects every user query for prompt injection attempts, including invisible characters, prompt breaks, and escape tokens.
Malicious patterns are rejected at the input layer, preventing jailbreaks or bypasses from entering the LLM context.
Not ChatGPT in a Green Suit
Let’s be clear. Qlik Answers is not simply a corporate version of ChatGPT. Here’s how it compares in critical areas:
Capability | ChatGPT / Gemini | Qlik Answers |
Retrieval from specific enterprise content | ❌ No | ✅ Yes |
Retrieval-gated generation | ❌ Optional | ✅ Enforced |
Source citations for every answer | ❌ Sometimes | ✅ Always |
Sanitization of PII / secrets | ⚠️ Partial | ✅ Enforced |
Guardrails for hallucinations | ⚠️ Limited | ✅ Multi-layered |
Prompt injection protection | ❌ No | ✅ Yes |
Role-based access to knowledge | ❌ No | ✅ Yes |
Multi-tenant deployment support | ⚠️ Cloud-specific | ✅ Designed for enterprise |
Structured Data Support: What’s Coming
As of June 2025, Qlik Answers supports unstructured content only: PDFs, Word docs, HTML, transcripts, and other natural language sources. But, as announced at Qlik Connect, structured data support is expected in the coming months, expanding its scope significantly.
Let’s emphasize: what follows is not based on current product capabilities, but on what has been publicly announced by Qlik and what I expect based on industry-standard architectural patterns. The final product might be different than this but I believe not by much (hopefully).
It will not be just a fancy Insight Advisor. Not at all. It will be something to a whole new level entirely. What Structured Data Support Might Look Like:
Qlik App Metadata Extraction: Dimensions, measures, KPIs, and chart descriptions from Qlik Sense applications would be indexed and embedded similarly to document chunks. These elements would carry metadata about chart type, filters, user ownership, and data lineage. It is also possible the chunks coming from the hypercubes directly.
Semantic Representation: Rather than just showing numbers, structured data points would be translated into narrative fragments like:
“Total revenue for Q2 2025 is $42.1M.”
“Customer churn increased by 4.2% in the northeast region.”
Hybrid Retrieval Strategy: The assistant would perform parallel lookups across:
Embeddings from unstructured documents (vector search via OpenSearch)
Structured metadata and facts from Qlik apps (potentially via semantic mapping or symbolic query planning).
Structured-Aware Prompt Construction: Retrieved structured facts would be assembled into prompt-ready context blocks. These might include numeric summaries, chart insights, or selected narrative captions tagged from dashboards.
Purpose and Context Tags: Structured assets would include tags like:
“Use only in executive summaries”
“Internal benchmarking only”
“Last updated on: May 10, 2025”
These tags would act as soft constraints in the generation layer, guiding the LLM not only on what to say but how to say it, depending on business context.
Conclusion: Trustworthy AI, Built for the Real World
Qlik Answers represents what enterprise AI should be: grounded, explainable, secure, and useful. It’s not trying to impress you with flashy prose or pretend it knows everything. Instead, it’s engineered to answer only what it actually knows.
Because it combines Retrieval-Augmented Generation with a stack of best-in-class technologies (OpenSearch for semantic search, Temporal for workflow orchestration, Claude for controlled generation, and a suite of guardrails for privacy and safety) Qlik Answers turns your company’s scattered knowledge into a unified, conversational interface.
It’s not here to replace dashboards or documentation. It’s here to make them searchable by meaning, answerable by question, and verifiable by source. The whole idea is to allow you to talk to your data and enable Language-Centric Analytics.
Structured data support is just around the corner, and with it, Qlik Answers is poised to evolve from document Q&A into a full-blown analytics assistant. One that understands charts, context, metrics, and models, all in the same breath.
So no, this isn’t just another LLM tool with a business wrapper. It’s a knowledge assistant that tells the truth or tells you it can’t. In the age of hallucinating chatbots, that’s not just helpful. That’s revolutionary. Welcome to the future!
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