Stop Scrolling: Build a Persona-Aware Tech Scout with Caching, Citations, and Prompt Chaining

Build a Niche Tech-Scouting Agent That Surfaces Real Signals

Ask a general chatbot to "scan tech and summarize what matters," and you'll get a generic roundup. That's because most assistants use broad search strategies and shallow sources. Researchers need repeatable pipelines, curated data, and controllable outputs.

Here's a practical workflow to build a niche agent that ingests millions of texts, filters them by a defined persona, and produces actionable themes with citations-without you scrolling forums or social feeds.

Why generic assistants miss the signal

• They pull from a handful of pages and recent headlines.
• They ignore context: your role, interests, and research horizon.
• They lack a vetted, high-coverage data source and a controlled workflow.

The fix is simple: build a narrow agent with a strong data moat, strict schemas, and prompt chaining. The goal is repeatability and signal density.

Data first: prepare and cache

Start with a dedicated data pipeline. Ingest thousands of tech forum posts and site updates daily. Use lightweight NLP to extract keywords, categories, and sentiment. Track keyword trends within categories over configurable windows (daily, weekly, monthly).

Add an endpoint that, for any keyword and time period, ranks sources by engagement, processes text in chunks, and preserves source citations. Summarize the kept "facts" with a final pass. Cache results so the first call takes seconds, and the rest return in milliseconds. This keeps report costs to cents, even at hundreds of keywords per day.

Citation-friendly "facts" endpoint

• Input: keyword + time window.
• Process: rank by engagement → chunk → keep/discard facts with small models → final summary with citations.
• Output: vetted facts with source links and stable IDs, ready for downstream LLMs.

This pattern mirrors citation engines (see an example in LlamaIndex docs) and makes verification easy.

Model sizing that saves money

• Use small models for routing, parsing to structured data, chunk-level keep/discard, and grouping/citation.
• Reserve strong reasoning models for final theme extraction and human-facing summaries.
• If a step falters, break it down and chain prompts. Smaller, single-purpose steps beat one oversized call.

Agent architecture at a glance

• Part 1: Setup (profile).
• Part 2: News (report).

Part 1 - Profile setup

Translate a short user summary into a strict schema. Use a system prompt that forces structured output, then validate. If validation fails, retry automatically. Store the result (a document store like MongoDB works well).

Recommended schema fields:

• Personality: short description of reading preferences (e.g., "skip jargon," "technical focus").
• Major categories: 2-4 high-level areas.
• Minor categories: optional, more granular.
• Keywords: up to 6, mapped to the data source.
• Time period: what the user requests (e.g., weekly).
• Concise summaries: boolean to control output length.

Why the schema matters: LLMs are great at translating natural language into JSON. Systems are great at validating JSON and routing data. Combine both and you get reliability.

Part 2 - Report generation

• Fetch profile → get categories and keywords.
• Pull top and trending keywords for the time window from the prepared store.
• Optional filter: a small LLM pass to drop irrelevant keywords (keep this tight to avoid noise).
• Call the cached "facts" endpoint for each keyword in parallel.
• Merge results, deduplicate facts, and normalize citations (keep keyword IDs stable).

Then run a two-step prompt chain:

• Step 1: Extract 5-7 themes ranked by profile relevance. Capture supporting points and the citation IDs.
• Step 2: Generate two summary lengths (concise and detailed) plus a clear title, referencing the original facts.

Only the final step uses a stronger reasoning model. Everything else runs on small, fast models-your cost driver stays low thanks to caching.

Caching, cost, and latency

• First call for a new keyword: up to ~30 seconds.
• Repeat calls: milliseconds.
• Run keywords in parallel to shorten wall-clock time.
• Daily cache refresh keeps reports fresh without reprocessing everything.

Engineering notes for researchers

• Define strict, versioned schemas and validate all LLM outputs.
• Prefer workflow graphs over free-form agents unless a human is in the loop.
• Keep inputs lean. Handing an LLM extra noise dilutes relevant outcomes.
• Log every step with artifacts (input text hashes, kept/discarded chunks, citations) for auditability.
• Measure: token usage per step, cache hit rate, time per report, and factual consistency across runs.

What this enables

• Fast literature-style tech scans without manual curation.
• Persona-aware trend tracking across forums, issue trackers, and community sites.
• Reproducible summaries with citations you can verify and share.

Extend the system

• Add human-in-the-loop steps for critical reviews (e.g., grant writing, clinical domains).
• Schedule recurring reports with diff views: new themes, rising keywords, decaying signals.
• Export structured outputs for downstream analysis and visualization.
• Gate long-context or reasoning calls behind cache checks to keep budgets predictable.

Quick build checklist

• Data pipeline with keyword, category, and sentiment extraction.
• Facts endpoint with chunking, engagement ranking, and citations.
• Cache layer with TTL, parallel calls, and stable IDs.
• Profile schema + validator + storage.
• Prompt chains: theme extraction → final summaries.
• Metrics: cost, latency, cache hit rate, factual consistency.

Further learning

• Prompt patterns and chaining strategies: Prompt Engineering resources.
• Citation workflows: LlamaIndex citation guide.

Build the agent once, keep the data clean, and let the cache do the heavy lifting. You'll get focused, verifiable insights while everyone else reads another generic roundup.