Who should use the Information Retrieval workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for information retrieval with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A deliverable information package that meets the original query need.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A deliverable information package that meets the original query need.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Scrintal to a clear, bounded query that guides all subsequent retrieval steps. Then, you pass the output to Brave Search AI to a configured retrieval environment ready to execute the query. Then, you pass the output to Brave Search AI to a set of candidate documents or passages that potentially answer the query. Then, you pass the output to Voyage AI to a concise, high-quality set of relevant results ready for extraction. Then, you pass the output to ChatGPT to a synthesized answer or summary with cited evidence from the retrieved sources. Then, you pass the output to LanguageTool to a validated, polished information product ready for delivery. Finally, Canopy is used to a deliverable information package that meets the original query need.
Define Query and Scope
A clear, bounded query that guides all subsequent retrieval steps.
Select and Configure Retrieval Sources
A configured retrieval environment ready to execute the query.
Execute Initial Retrieval
A set of candidate documents or passages that potentially answer the query.
Filter and Rank Results
A concise, high-quality set of relevant results ready for extraction.
Extract and Synthesize Information
A synthesized answer or summary with cited evidence from the retrieved sources.
Validate and Refine Output
A validated, polished information product ready for delivery.
Package and Deliver
A deliverable information package that meets the original query need.
Start by clarifying the exact information need: write a concise question or topic statement. Identify the domain, time range, and desired depth (e.g., overview vs. deep dive). This prevents aimless searching and sets success criteria.
Why Scrintal: Scrintal is designed for visual note-taking and knowledge organization, making it ideal for recording and structuring the query and scope of an information retrieval task.
Choose the appropriate databases, search engines, or knowledge bases based on the query scope. Configure API keys, access permissions, and any filters (e.g., date, relevance). For internal knowledge, map to vector stores or document indexes.
Why Brave Search AI: Brave Search AI provides real-time information synthesis and technical documentation retrieval, suitable for configuring web-based retrieval sources.
Run the query against selected sources using both keyword and semantic search methods. Collect top results (e.g., top 10-20 documents or passages). For RAG, retrieve relevant chunks from the vector store using cosine similarity.
Why Brave Search AI: Brave Search AI directly executes real-time web searches, fulfilling the need for a search engine to perform initial retrieval.
Apply relevance scoring (e.g., BM25, cosine similarity, or LLM-based relevance judgment) to rank results. Remove duplicates, low-quality sources, or off-topic entries. For RAG, re-rank chunks using cross-encoders or LLM scoring.
Why Voyage AI: Voyage AI specializes in reranking search results for improved relevance, directly addressing the need to filter and rank results.
Read through the top results and extract key facts, quotes, or data points. Synthesize into a coherent answer or summary, citing sources. For sentence-level retrieval, extract specific sentences that directly answer the query.
Why ChatGPT: ChatGPT excels at natural language generation and content creation, making it suitable for extracting and synthesizing information from retrieved results.
Check the synthesized answer for accuracy, completeness, and relevance against the original query. Cross-reference with additional sources if needed. For RAG, verify that retrieved chunks were correctly interpreted by the LLM.
Why LanguageTool: LanguageTool provides grammar checking, style guide enforcement, and tone adjustment, directly supporting validation and refinement of output.
Format the final output for the intended audience (e.g., report, email, slide, or API response). Include metadata like retrieval date, source list, and confidence level. For sentence-level retrieval, deliver a list of exact sentences with context.
Why Canopy: Canopy provides client management, document management, and workflow automation, suitable for packaging and delivering the final output.
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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