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

Chile is fast becoming a regional testbed for risk‑based, human‑centered AI in the public sector: a May 2024 draft AI bill and a national AI policy emphasize tiered risk classifications, transparency, and meaningful human oversight - a framework that shapes how agencies from SUSESO to technical advisory bodies will pilot real systems Chile AI regulation overview - May 2024 draft.

Practical experiments are already visible on the ground, from the SUSESO medical‑claims project that blends automation with human review to governance conversations led by GobLab UAI and the Ministry of Science; these pilots show how procurement, auditing and social‑service workflows must align with new compliance rules rather than simply chase efficiency SUSESO medical-claims project case study (Chile).

For public servants and vendors navigating this shift, building prompt and governance skills matters: Nucamp AI Essentials for Work bootcamp syllabus maps practical skills - prompting, risk awareness and tool use - into everyday government tasks so teams can turn regulation into trusted services.

Bootcamp	Length	Cost (early bird)	Courses
AI Essentials for Work	15 Weeks	$3,582	AI at Work: Foundations; Writing AI Prompts; Job-Based Practical AI Skills

Selection for the top 10 prompts and use cases weighed evidence-driven criteria: practical procurement readiness, workforce uptake, and governance safeguards.

Priority went to projects and prompts that matched the World Economic Forum's AI procurement playbook - “Procurement in a Box” - to ensure transparent vendor evaluation and room for new entrants (WEF AI procurement guidelines); to initiatives demonstrating real staff adoption and training rates from a MissionSquare survey of 2,000 public-sector employees (used to gauge likely operational uptake and reskilling needs) (MissionSquare survey of 2,000 government employees); and to frameworks that emphasize leadership, in‑house expertise and layered governance from the JRC's competency and governance work (used to weight each use case's risk and oversight needs) (JRC competency & governance frameworks).

Cases that combined measurable staff benefits, clear procurement paths, and explicit human‑in‑the‑loop controls ranked highest - because a prompt that saves time but erodes trust won't survive the Chilean compliance and procurement test; one memorable benchmark was simply: did a use case show training uptake or pilot data, not just a product pitch?

“As employers, helping employees understand the benefits of AI can be a critical component to unlocking new levels of productivity and job satisfaction across the workforce. Our research shows that AI is becoming more widely embraced by state and local government employees, with the most common uses leveraged for writing, document processing, and scheduling, among many more. As a result, there is a real opportunity for employers to lean in through proper training of available solutions and sharing more positive experiences about their AI offerings compared to the perceptions that may exist today.” - Gerald Young, senior researcher at the MissionSquare Research Institute

SUSESO's medical‑claims pilot in Chile shows how a risk‑based, human‑centered approach actually plays out: AI and predictive analytics can act as an

automated triage nurse

reading adjuster notes and attachments to surface early signals - surgery requests, an MRI order, or attorney involvement - that often foreshadow costly escalations, then running those scans continuously rather than waiting for manual review (Milliman claims triage guide: lowering workers' compensation costs with predictive analytics).

Crucially, the platform routes low‑confidence or high‑risk files to staff so humans keep final judgment, preserving explainability and avoiding opaque denials - a design point emphasized by practitioners who argue that

automation must augment, not replace, expertise

(Starmind blog: insurance claims automation and the role of human expertise in AI).

For procurement and local teams, the SUSESO medical‑claims project case study offers practical lessons on vendor evaluation, HITL workflows, and how to speed routine cases while protecting citizens from erroneous automation decisions (SUSESO medical-claims project case study: Chile government AI claims pilot).

This blend - automatic fast‑tracking of simple claims and human oversight for edge cases - keeps trust intact while cutting administrative friction.

For Chilean teams tackling mental‑health claims audits and bias reporting - such as GobLab UAI - a practical path to credibility runs through audit‑ready tooling and rigorous evaluation: centralize regulatory mappings and incident timelines so each flagged case has a defensible paper trail, use bias‑testing suites and prompt benchmarks to catch problematic responses, and keep humans in the loop for edge cases to protect users' rights and dignity.

Platforms that unify AI risk, privacy and compliance can shorten the gap between experiment and procurable service; for example, RadarFirst's regulatory risk management approach helps map controls to global frameworks and automate audit evidence RadarFirst regulatory risk management solution, while AWS's generative AI best‑practices and Audit Manager playbook show how to capture model invocation logs, apply guardrails, and run promptfoo‑style tests for fairness and accuracy so CloudWatch traces become courtroom‑ready artifacts AWS generative AI best practices and Audit Manager playbook.

The result: mental‑health claim reviews that are faster but also explainable and legally defensible - less guesswork, more timestamped evidence when outcomes matter most.

“The Radar platform is designed, built, and supported with security and privacy in mind. We understand the unique responsibility that we have as we help you simplify incident management.”

ChileCompra has become the gatekeeper that turns AI policy into procurement practice: its 2022 Standard Bidding Terms for Data Science and AI Projects forced bidders to surface bias metrics, transparency mechanisms and data‑protection plans - requirements that helped SUSESO insist on equity testing and explainability - before those templates were revoked in December 2023 to align with a new procurement law, leaving guidance non‑mandatory for now (ChileCompra Standard Bidding Terms for Algorithms and AI (GobLab UAI)).

That temporary rule change exposed a core trade‑off: traditional procurement scoring still tilts toward price and competition, but ChileCompra's mix of ethical guidance and a public Observatory is signaling that vendors who only compete on cost will struggle unless they can also demonstrate bias audits, data safeguards and experience on complex public projects - an outcome underscored in World Privacy Forum's reporting on how evaluation criteria shape real choices for agencies like SUSESO (World Privacy Forum report: AI Governance at Chile's SUSESO).

The Observatory's public monitoring and intelligent risk model make procurement visible - and costly mistakes harder to hide - so procurement teams and vendors alike now face a practical imperative: design procurements that reward trustworthy, explainable systems, not just low bids.

Metric	Value
Procedures monitored (2023 sample)	4,000
Flagged for correction/investigation	1,449 (36.6%)
Procurement value improved/corrected (2023)	~US$250 million
Observatory microsite visits increase after opening	+300%

“[In the beginning], looking at and monitoring the processes was a voluntary initiative,” says ChileCompra's Director, Verónica Valle.

Workhelix's task-based scan offers a practical way for Chilean agencies to shrink bureaucracy and target automation where it actually helps: the platform ingests job titles, task lists and HR data to produce a slide deck, PDF and dashboard that point to low‑risk, high‑benefit pilots - turning fuzzy “digital transformation” talk into an evidence-backed roadmap (Workhelix – your partner for AI success).

That kind of granular mapping helps procurement teams prioritize pilots that match ChileCompra's trust-and-transparency expectations and gives units from SUSESO to education ministries a defensible basis for vendor selection and reskilling plans (see the SUSESO medical‑claims case study for how human-in-the-loop design mattered) (SUSESO medical-claims project case study).

Workhelix's research also urges choosing projects with attractive benefit‑to‑cost ratios and rapid iteration, a method that produced measurable productivity gains in media‑reported pilots - useful guidance when teams must show concrete wins before scaling (Workhelix research and media coverage).

“The best way to begin learning [how to interact with LLMs] is to find a project with an attractive benefit-to-cost ratio and low risks and start trying things.” - Daniel Rock, et al.

SENCE and the Ministry of Education can use teacher administrative automation to cut paperwork and keep human judgment where it matters most: deploy routine form‑filling, attendance logs and reporting templates behind transparent, human‑in‑the‑loop gates - mirroring procurement and oversight lessons from the SUSESO medical‑claims project case study - so ambiguous or high‑risk cases route to staff with full audit trails.

Framing automation as a workforce strategy, not a cost‑only play, aligns with Chile's 2025 emphasis on how to reskill public servants for high‑value tasks, and echoes advice given to roles like Junior Policy Analysts - upgrade toward evaluation, stakeholder engagement and contextual judgement.

Done well, teacher automation becomes a trustable tool that preserves oversight, creates clear procurement requirements for vendors, and buys teachers more time for students rather than paperwork.

A citizen‑facing chatbot at SUSESO or the Ministerio del Trabajo can speed access to benefits while protecting livelihoods - but only if it's built to reflect Chile's hard lessons about procurement, human oversight and accuracy.

With roughly 200,000 medical‑leave claims passing through SUSESO last year and some 20,000 still awaiting decisions, a chatbot that offers instant guidance needs a robust knowledge base and retrieval‑augmented LLM architecture so answers are grounded in current rules and case files (Knowledge Bases and Retrieval‑Augmented LLMs: implementation primer).

Procurement realities from World Privacy Forum's analysis show why chatbots must include human‑in‑the‑loop escalation paths and vendor transparency - ChileCompra's bidding templates reshaped vendor behavior but also revealed tensions between cost and responsible‑AI criteria (World Privacy Forum analysis of SUSESO procurement and responsible AI).

Practical examples from nearby governments (for instance, the REVE Chat deployment that handled thousands of sessions in its first month) illustrate scale and the need for monitoring, audit trails and WhatsApp‑friendly channels as part of any citizen service rollout (REVE Chat government chatbot deployment case study).

“These types of models or systems should be employed only in support of, or to inform, how a human - possibly someone with relevant subject‑matter expertise - ultimately makes the decision.”

Chile's Ministerio de Ciencia can turn the National AI Policy's paperwork - risk classifications, audit records and transparency obligations - into practical, audit-ready dashboarding and automated report generation so policy teams spot high-risk systems and evidence compliance before procurement or parliamentary review; this approach directly serves the policy pillars in the National AI Policy and the May 2024 bill by turning documentation and human-oversight requirements into searchable artifacts (Chile AI regulation: risk classifications and compliance details) and by delivering the quick wins Weintraub identifies - visualizations and reports that free analysts for strategic work (Stanford study on generative AI impact in Chile by Weintraub).

Well-designed visualizations not only speed routine monitoring across the policy's three pillars (enabling factors, adoption, ethics) but create timestamped, shareable outputs that make audits and procurement debates less abstract - imagine converting dense compliance annexes into a single weekly dashboard that flags models needing human review before they reach citizens' lives.

The Ministerio de Ciencia can make Chile's National AI Policy operational by turning dense regulatory rules - risk classifications, prohibited systems, transparency obligations and human‑oversight requirements - into searchable, audit‑ready products: retrieval‑augmented summaries that flag high‑risk models, produce timestamped compliance briefs for procurement reviews, and generate the plain‑language synopses legislators and policy teams need to act quickly; the official AI framework already stresses risk‑based classification and alignment with ISO/IEC standards, so tools that extract governance artifacts from documentation map directly onto those obligations (Chile AI regulation: risk classifications & compliance).

By indexing the National AI Policy's three pillars and feeding legal texts into an information‑retrieval pipeline, teams can surface which systems require formal authorisation (and prepare the dossiers the bill envisions, including the 60‑day review window), shortening review cycles and making audits less abstract (Tech law analysis: authorisation process and requirements in Chile); this work sits squarely on the National AI Policy's roadmap for enabling, adopting and ethically governing AI (Chile National AI Policy 2021–2030 overview), and it can transform compliance from a paperwork burden into a strategic asset for Chilean teams.

National AI Policy Pillar	Purpose
Enabling factors	Build capacity, standards alignment, infrastructure
Development & adoption	Support practical AI use while managing risks
Ethics, regulation & socio‑economic impact	Ensure transparency, human oversight, and safeguards

CENIA's Latam‑GPT effort is a Chile‑led push to fine‑tune large language models so they actually speak Chilean Spanish, Indigenous tongues and local ways of saying things - not just formal Spanish scraped from Spain - by training on regionally sourced corpora and community partnerships that include island‑scale projects like an AI translator for Rapa Nui on Easter Island; the project ingests concentrated regional data (reports list roughly 8 TB of text and tens of billions of tokens/parameters) and aims to fix the “cultural blindspots” of global models while giving schools, courts and government services more accurate, locally aware responses (see reporting on Latam‑GPT's regional build and goals) How Latam‑GPT is building culturally relevant AI.

Fine‑tuning can sharpen relevance and reduce hallucinations for Chilean use cases, but it needs high‑quality, human‑labelled data and careful validation to avoid overfitting - lessons illustrated by supervised fine‑tuning projects that used structured human preference rankings across dialects (Appen multilingual fine‑tuning case study) - and the compute backbone (hosted in northern Chile) raises real operational and environmental trade‑offs that policy and procurement teams must weigh (Rest of World coverage of Latam‑GPT).

Metric	Reported value
Regional data ingested	~8 TB (regional corpora)
Tokens / scale	~70 billion tokens (reported)
Model parameters	~50 billion parameters (assembled)
Documents reported processed	~3 million – >1 billion (sources vary)
Indigenous languages included	Rapa Nui, Mapudungun, Quechua, Nahuatl (and others)

“It shouldn't be the person who adapts to the technology, it should be the technology adapting to them.”

Equinix's planned ST5 campus in Pudahuel reframes how Chilean planners and procurement teams should write infrastructure‑planning prompts: require not just capacity numbers but explicit resilience, energy and interconnection commitments - ST5 is a $130M project with 16 data rooms across two floors, about 24,420 sqm of built area on a 42,400 sqm site, and hardened power (16 emergency generators plus a 38‑hour fuel tank) that speaks to continuity risk management (DatacenterDynamics: Equinix plans $130M data center in Santiago, Chile).

Prompts for vendors and policy teams should therefore demand local‑grid and renewables sourcing, low‑latency network peering, and environmental approvals aligned with Chile's national datacenter roadmap so projects become regional hubs rather than isolated facilities - an outcome analysts say could reposition Santiago as a Southern Cone interconnection node (Datacenters.com analysis: What Equinix's investment means for South American data center real estate).

A practical planning prompt: ask bidders to map energy mix, show interconnection options with existing IBX sites, and supply staged handover dates (ST5 targets start May 2025 with first‑phase delivery April 2027) so public agencies can align procurement timelines with capacity and regulatory milestones.

Spec	Value
Estimated cost	$130 million
Land area	42,400 sqm (456,000 sq ft)
Built area	24,420 sqm (263,000 sq ft)
Data rooms	16 (two floors)
Power redundancy	16 emergency generators; 1 office generator (900 kVA)
Fuel storage	38‑hour tank
Construction start	May 2025
First phase completion	April 2027

Chile's next phase is practical: prioritize collaboration, pilot governance assets, and bake procurement‑ready controls into every project so models are tested in context before they scale.

GobLab's long‑running Ethical Algorithms work has already produced concrete tools - from an Algorithmic Transparency Report Card to bias‑measurement suites - that show how public procurement can reward explainability and fairness rather than just low price (GobLab Ethical Algorithms report on fair and transparent AI in Chile); those lessons sit naturally beside the country's emerging risk‑based framework and the May‑2024 bill that demands human oversight, documentation and tiered compliance for high‑risk systems (Chile AI regulation overview (May 2024 AI bill)).

Practical next steps for agencies and vendors: run small, audited pilots that create timestamped evidence for procurement, adopt evaluation environments to vet governance tools, and invest in workforce reskilling so staff move from form‑filling to oversight - training such as Nucamp's AI Essentials for Work bootcamp maps exactly to those needs by teaching prompt design, risk awareness and tool use for everyday public‑sector tasks (Nucamp AI Essentials for Work bootcamp syllabus).

The payoff is simple but powerful: pilots that prove fairness and explainability turn compliance from a cost into a credibility advantage for Chile's public services.

Bootcamp	Length	Cost (early bird)	Courses
AI Essentials for Work	15 Weeks	$3,582	AI at Work: Foundations; Writing AI Prompts; Job-Based Practical AI Skills

“This not only improves the efficiency of government acquisitions but also strengthens public trust in government management and fosters equal opportunities for suppliers and contractors.”