Top 10 AI Prompts and Use Cases and in the Government Industry in Argentina

Argentina's public sector is turning a spotlight on practical, people-first AI: from Buenos Aires' WhatsApp “Boti” for city services to a Google Green Light pilot that cut stops by about 14% and saved thousands of commute hours and liters of fuel, showing how AI can shave costs and emissions in urban mobility (see the PANTA deep-dive on Argentina AI use cases).

The national push is backed by a formal Plan Nacional de Inteligencia Artificial that promotes inclusive, ethical adoption and talent development, while a “cloud‑first” push and ARSAT data‑center plans aim to give government projects the infrastructure they need to scale.

Still, ambition meets caution - regulators, civil society, and policymakers are balancing innovation with privacy, auditability, and the very real risks of brain drain and funding volatility.

For a concise overview, read the Argentina's digital economy briefing and the OECD summary of the national AI plan.

Initiative	Plan Nacional de Inteligencia Artificial
Start Year	2019
End Year	2030
Estimated Annual Budget	€12,500,000
Target Sectors	Public governance, Education, Inclusive development, Economy

Let's not overregulate ourselves

Selection began with a simple rule: pick Argentina-relevant pilots that promise clear citizen benefit and measurable KPIs, then test prompts against conversational and system-level metrics.

Use cases were screened for scale (Buenos Aires traffic-light optimization), citizen-facing impact (ANSES/WhatsApp-style call-center paths) and governance constraints; prompt designs were iterated with A/B tests and conversation-log analysis to expose failure modes.

Evaluation relied on the 14-chatbot metrics playbook - average conversation length, human takeover rate, engaged conversations and goal completion - as well as multi-turn LLM metrics like role adherence, conversation relevancy, knowledge retention and conversation completeness from the DeepEval methodology; regression tests and automated reports helped catch regressions early.

Practical steps included mining logs for frequent questions, running sliding-window relevancy checks, and tuning prompts to reduce missed messages and boost task-completion rates.

For readers building public-sector bots, the combined approach - real-world pilots plus rigorous multi-turn evaluation - keeps projects accountable and focused on citizens, not just technology (see the 14 chatbot metrics to track AI customer service performance, DeepEval LLM chatbot evaluation metrics and testing techniques, and the Buenos Aires traffic-light optimization pilot study).

Chatbots are one specific type of conversational interface with no explicit goal other than engaging the other party in an interesting or enjoyable conversation.

Boti is the City of Buenos Aires' WhatsApp-savvy conversational assistant that turns the tangle of citizen questions - driver's-license renewals, health info, parking permits, Ecobici and even pandemic triage - into fast, local-feeling answers while routing tougher cases to human teams; see the official Boti WhatsApp assistant overview on Botmaker.

Recent modernizations pair Bedrock-powered generative responses with an input guardrail and a “reasoning retriever” that disambiguates similar procedures so users get the right steps, in Rioplatense Spanish with voseo, concise lists, and even emojis to make instructions stick - a practical mix of safety and usability described in the Amazon Bedrock write-up (Amazon Bedrock case study: Boti AI assistant).

The result is a city bot that scales - millions of interactions per month - and trims human load while improving retrieval accuracy for confusing procedures, a model other municipal teams can study and adopt.

Metric	Value (source)
Launch year	2019 (govlaunch)
Monthly interactions	2M–3M (Microsoft / AWS)
Operational load reduction	~50% (Microsoft)
Voseo accuracy (SME)	98% (AWS Bedrock)

"Boti was our bridge between the government and the citizens when we needed it most." - Pedro Alessandri, Undersecretary of Smart City, GCBA

Procedures RAG paired with a reasoning retriever is the practical glue that turns Argentina's pile of statutes, municipal forms and help‑desk transcripts into an “open‑book” assistant that points citizens and clerks to the exact rule or step they need - reducing mistaken applications and repeated phone calls while preserving audit trails.

At its core this combines fast vector retrieval and re‑ranking with a grounded generator so answers are current and cite source passages (see a clear primer on RAG systems from Datategy), and careful data governance - metadata, RBAC, versioning and provenance - to keep sensitive records compliant (Enterprise Knowledge's governance playbook is a useful reference).

The technical pattern (ingest → embeddings → semantic search → context‑aware generation) is well suited to form guidance, multi‑turn clarifications and escalation to humans, and cloud vendors now offer RAG toolchains and eval services to score groundedness and relevancy as you iterate (see Google Cloud's RAG overview).

The memorable payoff: instead of a long bureaucratic paper chase, users get concise, sourced next steps - like being handed the exact clause and the three fields to complete next.

RAG Component	Why it matters for government procedures
Retriever / Vector search	Finds up‑to‑date, semantically relevant clauses and forms
Generator & prompts	Turns retrieved excerpts into plain‑language, step‑by‑step guidance
Data governance (RBAC, lineage)	Ensures auditability, sovereignty and safe scaling

“With LAFT, we bridge the gap between adaptability and stability, enhancing domain-specific performance without compromising the foundational intelligence of large language models.” - Mohamed Elhawary, R&D Data Scientist

Buenos Aires has quietly tested Google's Project Green Light with tangible local wins: small timing tweaks (for example, synchronizing Tronador Street with Melián Avenue) cut stops and emissions without new hardware, making it a low‑cost, fast win while bigger transport projects progress; a stretch on Ruiz Huidobro Avenue recorded 14% fewer stops, 2,339 hours saved in annual travel time and 6,987 litres of fuel spared, and the program's early data suggest broader potential of up to 30% fewer stops and as much as a 10% reduction in intersection emissions.

The system uses a decade of Google Maps driving trends to recommend short, engineer‑reviewable signal changes that cities can implement in minutes, and it's already live in over 70 intersections worldwide - a pragmatic interim step for Argentine cities while metropolitan rail and cycle networks catch up (see the Buenos Aires pilot write‑up on Context News, Google's Project Green Light overview, and a local summary of the traffic‑light optimization pilot for more detail).

Metric	Value
Live intersections (global)	70+
Cities piloting Green Light	17 (global)
Ruiz Huidobro measured impact	14% fewer stops; 2,339 hours saved; 6,987 L fuel saved annually
Reported potential reductions	Up to 30% fewer stops; up to 10% lower emissions at intersections
Pilot start	2021

"You know when you drive through a sequence of five green lights, and it feels like your lucky day?" - Dotan Emanuel, Google Research

Predictive policing promises sharper resource allocation but carries real risks for Argentina's cities: models trained on historical arrest data can re‑entrench over‑policing of marginalized communities and erode trust unless strict guardrails are put in place, a pattern documented by civil‑rights researchers and policy analysts (see the NAACP issue brief on predictive policing and CIGI's review of promises and perils).

Practical city teams tempted by potential gains - lowered crime maps or faster response times as highlighted in global pilots - should pair any experiments with mandated transparency, independent audits, community consultation, and clear limits on the types of data used; industry guidance and ethics frameworks stress white‑box algorithms, RBAC, and continuous third‑party testing as minimum safeguards (see Deloitte's ethical implementation overview).

The memorable trade‑off is stark: a system that might shave response times can, without oversight, turn a neighbourhood into an algorithmic hotspot overnight - so oversight, disclosure and auditability aren't optional extras, they're the baseline for any Argentine pilot.

Implement Rigorous Oversight: Establish independent oversight bodies to review and monitor the use of AI in policing, ensuring algorithms are fair, accurate, ...

Entelai-style, AI-assisted medical imaging platforms bring practical wins for public health by acting as a reliable second pair of eyes: streamlining radiology workflows, flagging urgent findings for faster care-team activation, and tying alerts into hospital systems so follow‑up actually happens; see Aidoc AI radiology platform that prioritizes findings and integrates with EHR and PACS.

Clinical research supports this triage value too - deep‑learning analysis of chest X‑rays improved prediction of 30‑day adverse outcomes and safely deferred additional testing for a meaningful share of low‑risk patients, a clear efficiency gain in crowded emergency departments (RSNA study on AI triage for chest X‑rays and 30‑day outcome prediction).

For Argentina's public hospitals, the practical payoff is vivid: a background model quietly scanning every film and lighting up the single case an overwhelmed ER must not miss - faster routing, fewer unnecessary tests, and clearer paths for follow‑up that together improve outcomes without adding staff burden.

“Analyzing the initial chest X-ray of these patients using our automated deep learning model, we were able to provide more accurate predictions regarding patient outcomes as compared to a model that uses age, sex, troponin or d-dimer information.” - Dr. Márton Kolossváry

Satellite imagery is a practical, high‑value tool for Argentina's agriculture and disaster teams: analyses like Streambatch's post on the 2023 Argentina drought used NDVI trends to quantify how soybean greenness fell over time, turning maps from green to rust‑brown in just weeks and flagging regions that need urgent support (Streambatch 2023 Argentina drought NDVI analysis).

Remote sensing indices - NDVI for vegetation vigor and VHI to capture temperature stress - are already standard inputs for yield signals and early‑warning systems (see the CropProphet satellite NDVI and VHI primer), and commercial monitoring platforms offer the resolution and cadence governments need: daily PlanetScope updates plus 3–10 m spatial detail and 15+ vegetation indices that let agronomists and emergency responders detect water stress, target irrigation, speed insurance assessments, and prioritize field inspections (CropProphet satellite NDVI and VHI primer, EOSDA satellite imagery for precision agriculture monitoring).

The memorable payoff for cities and provinces: a satellite alert that turns a sprawling, uncertain harvest forecast into a clear, 8‑day map of where to send technicians now.

Capability	Detail (source)
Spatial resolution	3 m (PlanetScope) – 10 m (Sentinel‑2) (EOSDA)
Update frequency	Daily (PlanetScope) / every 3–5 days (Sentinel‑2) (EOSDA)
Key indices	NDVI, VHI and 15+ vegetation indices (CropProphet / EOSDA)
Applied use cases	Drought tracking, yield signals, targeted irrigation, damage assessment (Streambatch / EOSDA / CropProphet)

Procurement anomaly detection is becoming a practical, high‑impact lever for Argentine public teams: recent research and vendor write‑ups show that combining pattern‑mining and graph‑based link analysis with real‑time anomaly scoring and NLP on contracts and invoices can surface the subtle schemes that human audits miss - from ghost suppliers and split orders to bid‑rigging and forged invoices.

A systematic mapping study of data‑driven fraud detection gives a clear taxonomy of these methods and where they work best (EPJ Data Science systematic mapping study on public procurement fraud detection), while practical guides illustrate how basic analytics and link‑analysis checks can reveal thousands of suspicious records (for example, vendors sharing addresses or bank accounts) and cut risk before money leaves the treasury (SAS procurement fraud prevention guide for contracts and procurement).

A memorable payoff: continuous monitoring that flags an unusual cluster or a phantom vendor can turn months‑long leakage into a single, investigable alert - cheaper and faster than chasing problems after the fact.

Technique	What it catches	Why it matters
Pattern mining / anomaly detection	Duplicate invoices, price spikes, split POs	Detects outliers at scale for early intervention
Graph / link analysis	Ghost suppliers, shared accounts/addresses	Reveals collusion and hidden ownership networks
NLP & text mining	Contract changes, suspicious clauses, invoice forgeries	Automates review of unstructured procurement documents
Continuous monitoring & scoring	Real‑time suspicious transactions	Makes fraud prevention proactive, not reactive

The SNRP (Fugitive Facial Recognition System) saga in Buenos Aires is a cautionary chapter for any public‑sector AI program: a 2019 rollout of 300 cameras that scanned live feeds against the CONARC fugitives database was suspended and later ruled unconstitutional after courts found widespread privacy violations, unreliable data and abuse of authority, including wrongful arrests and undocumented deletions of search records (see the FPF ruling summary and contemporaneous reporting by Context News report on the Buenos Aires facial recognition reboot and BiometricUpdate coverage of the Buenos Aires facial recognition network).

The court required a formal oversight framework - an audit plan, a public registry of surveillance systems, a Special Monitoring Committee and a data‑protection impact assessment - before any reactivation; civil society groups and the city government have since clashed over whether a “black‑box” test is sufficient.

The human cost is stark: one man, Guillermo Ibarrola, was misidentified, whisked away hundreds of miles and spent days in custody because of a data error - an image that turned abstract risk into a very real nightmare for a family.

The takeaway for Argentine policymakers is clear: live facial recognition in public spaces can only be defensible with transparent governance, third‑party audits, strict limits on databases and documented chains of access and accountability.

Metric	Value (source)
Cameras	300 (Context / Japan Times)
Implemented	2019 (FPF)
Suspended / ruled unconstitutional	2022 (FPF)
CONARC database size	~40,000 records (Context)
Biometric data requests logged	9,392,372 vs ~35,000 active CONARC registries (FPF)
Reported matches / detections	~1,600–1,700 (city claim; reporting)

"It was a nightmare." - Guillermo Ibarrola

For a civic newsroom like 0221, modern AI transcription tools turn long council hearings, press conferences and public‑record audio into searchable, time‑stamped transcripts with speaker labels, concise summaries and auto‑extracted action items - cutting the time editors spend on verbatim note‑taking by as much as 80–90% while making every quote and decision easy to verify (see Convene's roundup of secure meeting transcription capabilities).

Speaker diarization - the ability to identify and separate multiple speakers without prior setup - adds crucial clarity for multi‑speaker recordings and helps attribute statements accurately for accountability and follow‑ups (Fano's primer explains why diarization is central to multi‑speaker understanding).

Built‑in features to handle multilingual speech and code‑switching, redaction and auditable, encrypted logs mean transcripts can feed legal‑record workflows and public‑interest investigations without sacrificing privacy or compliance; the practical payoff is immediate: faster, more transparent reporting and a newsroom that can turn raw civic audio into verified, shareable public records in hours, not days.

Argentina's national AI push stitches policy, talent and inclusion into a single practical agenda: the Plan Nacional de Inteligencia Artificial (part of Innovative Argentina 2030 and the 2030 Digital Agenda) aims to boost inclusive, ethical AI adoption, develop AI talent, and coordinate federal R&D while protecting privacy and labour markets - details summarized in the OECD AI National Plan overview for Argentina.

Policymakers are pairing an emerging national AI Innovation Hub and an estimated €12.5M/year budget with concrete guidance such as the public sector's “Recommendations for Trustworthy Artificial Intelligence” (Disposición No.

2/2023) to reduce bias and preserve rights (see the PANTA analysis of Argentina's AI ambitions).

Workforce reskilling and digital inclusion are front and centre - the administration has signalled large-scale training goals (for example, plans to train 1 million people in digital skills by 2026) - but the path is fragile: funding swings and brain drain mean strategy must pair ambition with clear oversight, impact evaluation and federal coordination so benefits reach schools, hospitals and small firms, not just urban labs.

Item	Value (source)
Start Year	2019
End Year	2030
Estimated annual budget	€12,500,000
Status	Active
Target sectors	Public governance; Education; Inclusive development; Economy

“The National Artificial Intelligence (AI) Strategy... spells out our plans to deepen our use of AI technologies to transform our economy, going beyond just adopting technology, to fundamentally rethinking business models and making deep changes to reap productivity gains and create new areas of growth.”

Practical next steps for Argentine government teams and beginners: start small, measure everything, and pair clear citizen benefits with hard guardrails - pilot projects like Buenos Aires' traffic tweaks and Boti show how targeted experiments can cut stops and operational load while keeping the public onside (see the PANTA deep-dive on Argentina's AI ambitions and the Amazon Bedrock write-up on Boti's conversational assistant).

Technically, pair RAG-style retrieval with a reasoning retriever so procedures return the exact clause and “the three fields to complete next” rather than vague answers; operationally, require transparency, independent audits and RBAC before scaling, especially for security or surveillance projects.

Invest in people: short, practical courses help teams move from theory to deployable prompts - Nucamp's AI Essentials for Work is a 15‑week, practitioner-focused option to learn prompting, tools and workplace applications (early-bird pricing and registration info available Nucamp AI Essentials for Work registration and early-bird pricing).

The tight sequence - pilot, measure, audit, reskill - turns ambition into durable public value while guarding rights and retaining talent.

Step	Why it matters
Pilot citizen-facing use cases	Delivers measurable wins (mobility, chatbots) and builds trust
Use RAG + reasoning retriever	Improves procedural accuracy and cites sources for auditability
Mandate transparency & independent audits	Prevents misuse in security/surveillance applications
Reskill staff with practical courses	Makes deployments sustainable and reduces brain-drain risk

"Boti was our bridge between the government and the citizens when we needed it most." - Pedro Alessandri